2022.04.19.txt

1, TITLE: Intelligent Explorations of The String Theory Landscape
AUTHORS: Andrei Constantin
CATEGORY: hep-th [hep-th, cs.AI]
HIGHLIGHT: In this review we focus on model building efforts in the context of the $E_8\times E_8$ heterotic string compactified on smooth Calabi-Yau threefolds and discuss several areas in which machine learning is expected to make a difference.

2, TITLE: Boson Sampling Cannot Be Faithfully Simulated By Only The Lower-order Multi-boson Interferences
AUTHORS: Valery Shchesnovich
CATEGORY: quant-ph [quant-ph, cs.CC, math-ph, math.MP]
HIGHLIGHT: The distinguishing datasets can be the experimental estimates of some large probabilities, a wide class of such is presented. This is a sequel of \textit{Quantum} \textbf{5}, 423 (2021), where I present more accessible account of the main result enhanced by additional insight on the contribution from the higher-order multi-boson interferences in presence of noise.

3, TITLE: Strengthening Subcommunities: Towards Sustainable Growth in AI Research
AUTHORS: Andi Peng ; Jessica Zosa Forde ; Yonadav Shavit ; Jonathan Frankle
CATEGORY: cs.AI [cs.AI, cs.LG]
HIGHLIGHT: These challenges largely center on the inability of specific subareas to identify and evaluate work that is appropriate according to criteria relevant to each subcommunity as determined by stakeholders of that subarea. We set forth a proposal that re-focuses efforts within these subcommunities through a decentralization of the reviewing and publication process.

4, TITLE: Cognitive Architecture for Decision-Making Based on Brain Principles Programming
AUTHORS: Anton Kolonin ; Andrey Kurpatov ; Artem Molchanov
CATEGORY: cs.AI [cs.AI, cs.CE]
HIGHLIGHT: We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.

5, TITLE: Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction
AUTHORS: Nikhil Krishnaswamy ; Sadaf Ghaffari
CATEGORY: cs.AI [cs.AI, cs.CV, cs.LG]
HIGHLIGHT: In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction.

6, TITLE: Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in Artificial Intelligence
AUTHORS: Jennafer S. Roberts ; Laura N. Montoya
CATEGORY: cs.AI [cs.AI, cs.CY, cs.GL, cs.HC, cs.NE]
HIGHLIGHT: In this meta-ethnography, we explore three different angles of Ethical AI design and implementation in a top-down/bottom-up framework, including the philosophical ethical viewpoint, the technical perspective, and framing through a political lens.

7, TITLE: DialAug: Mixing Up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling
AUTHORS: Lahari Poddar ; Peiyao Wang ; Julia Reinspach
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper we propose a framework that incorporates augmented versions of a dialogue context into the learning objective.

8, TITLE: Ìtàkúròso: Exploiting Cross-Lingual Transferability for Natural Language Generation of Dialogues in Low-Resource, African Languages
AUTHORS: TOSIN ADEWUMI et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: The main contributions of this paper include the representation (through the provision of high-quality dialogue data) of under-represented African languages and demonstrating the cross-lingual transferability hypothesis for dialogue systems.

9, TITLE: StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
AUTHORS: Zhengxiang Shi ; Qiang Zhang ; Aldo Lipani
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this paper, we present a new Question-Answering dataset called StepGame for robust multi-hop spatial reasoning in texts.

10, TITLE: Contrastive Learning with Hard Negative Entities for Entity Set Expansion
AUTHORS: YINGHUI LI et. al.
CATEGORY: cs.CL [cs.CL, cs.IR]
HIGHLIGHT: Although previous ESE methods have achieved great progress, most of them still lack the ability to handle hard negative entities (i.e., entities that are difficult to distinguish from the target entities), since two entities may or may not belong to the same semantic class based on different granularity levels we analyze on. To address this challenge, we devise an entity-level masked language model with contrastive learning to refine the representation of entities.

11, TITLE: Evaluating Mixed-initiative Conversational Search Systems Via User Simulation
AUTHORS: Ivan Sekuli? ; Mohammad Aliannejadi ; Fabio Crestani
CATEGORY: cs.CL [cs.CL, cs.IR]
HIGHLIGHT: However, evaluation of such systems through answering prompted clarifying questions requires significant human effort, which can be time-consuming and expensive. In this paper, we propose a conversational User Simulator, called USi, for automatic evaluation of such conversational search systems.

12, TITLE: CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning
AUTHORS: Siddharth Verma ; Justin Fu ; Mengjiao Yang ; Sergey Levine
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: In this paper, we study how offline reinforcement learning can instead be used to train dialogue agents entirely using static datasets collected from human speakers.

13, TITLE: On Effectively Learning of Knowledge in Continual Pre-training
AUTHORS: CUNXIANG WANG et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: Then, we find that PLMs are more likely to give wrong predictions on K-B tokens and attend less attention to those tokens inside the self-attention module. Based on these observations, we develop two solutions to help the model learn more knowledge from unstructured text in a fully self-supervised manner.

14, TITLE: Learning to Classify Open Intent Via Soft Labeling and Manifold Mixup
AUTHORS: Zifeng Cheng ; Zhiwei Jiang ; Yafeng Yin ; Cong Wang ; Qing Gu
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To address the challenge that training a (K+1)-class classifier with training samples of only K classes, we propose a deep model based on Soft Labeling and Manifold Mixup (SLMM).

15, TITLE: Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models
AUTHORS: Keigo Takahashi ; Danushka Bollegala
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We propose a sentence-level meta-embedding learning method that takes independently trained contextualised word embedding models and learns a sentence embedding that preserves the complementary strengths of the input source NLMs.

16, TITLE: WikiOmnia: Generative QA Corpus on The Whole Russian Wikipedia
AUTHORS: Dina Pisarevskaya ; Tatiana Shavrina
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We present the WikiOmnia dataset, a new publicly available set of QA-pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generative pipeline.

17, TITLE: TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
AUTHORS: Megan Leszczynski ; Daniel Y. Fu ; Mayee F. Chen ; Christopher Ré
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: In this work, we introduce TABi, a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks.

18, TITLE: Towards Unification of Discourse Annotation Frameworks
AUTHORS: Yingxue Fu
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To make better use of the existing discourse corpora and achieve the possible synergy of different frameworks, it is worthwhile to investigate the systematic relations between different frameworks and devise methods of unifying the frameworks.

19, TITLE: SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words
AUTHORS: Renliang Sun ; Xiaojun Wan
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we propose a continued pre-training method for text simplification.

20, TITLE: A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems
AUTHORS: Debjoy Saha ; Bishal Santra ; Pawan Goyal
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented conversational systems.

21, TITLE: UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue
AUTHORS: Chang Gao ; Wenxuan Zhang ; Wai Lam
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: However, such pipeline methods would unavoidably suffer from the error propagation issue. This paper proposes to unify these two sub-tasks via sequentially generating the grounding knowledge and the response.

22, TITLE: TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark
AUTHORS: ANIA WRÓBLEWSKA et. al.
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: However, there are still only a few well-defined tasks that serve as benchmarks for solutions in this area. We introduce a new dataset -- called \textit{TASTEset} -- to bridge this gap.

23, TITLE: UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation
AUTHORS: Amirhossein Abaskohi ; Arash Rasouli ; Tanin Zeraati ; Behnam Bahrak
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: The methodology and results of our team, UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in this paper.

24, TITLE: TVShowGuess: Character Comprehension in Stories As Speaker Guessing
AUTHORS: YISI SANG et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories.

25, TITLE: Learning to Execute or Ask Clarification Questions
AUTHORS: Zhengxiang Shi ; Yue Feng ; Aldo Lipani
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.

26, TITLE: Ingredient Extraction from Text in The Recipe Domain
AUTHORS: Arkin Dharawat ; Chris Doan
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Extracting these recipe-relevant aspects from the query thus becomes important when it comes to addressing the user's information need. Our project focuses on extracting ingredients from such plain-text user utterances.

27, TITLE: Non-Parallel Text Style Transfer with Self-Parallel Supervision
AUTHORS: Ruibo Liu ; Chongyang Gao ; Chenyan Jia ; Guangxuan Xu ; Soroush Vosoughi
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: In this work, we propose LaMer, a novel text style transfer framework based on large-scale language models.

28, TITLE: Less Is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
AUTHORS: Hanxun Zhong ; Zhicheng Dou ; Yutao Zhu ; Hongjin Qian ; Ji-Rong Wen
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information.

29, TITLE: Back to The Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling
AUTHORS: Yiyang Li ; Hai Zhao ; Zhuosheng Zhang
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks.

30, TITLE: Nested Named Entity Recognition As Holistic Structure Parsing
AUTHORS: Yifei Yang ; Zuchao Li ; Hai Zhao
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Besides, there is no research on applying corpus-level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpus-level.

31, TITLE: Pathologies of Pre-trained Language Models in Few-shot Fine-tuning
AUTHORS: Hanjie Chen ; Guoqing Zheng ; Ahmed Hassan Awadallah ; Yangfeng Ji
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from. In this work, we propose to answer this question by interpreting the adaptation behavior using post-hoc explanations from model predictions.

32, TITLE: Factual Error Correction for Abstractive Summaries Using Entity Retrieval
AUTHORS: HWANHEE LEE et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we propose an efficient factual error correction system RFEC based on entities retrieval post-editing process.

33, TITLE: BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling
AUTHORS: CUNLIANG KONG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We propose a transformer-based multitasking framework to explore the task.

34, TITLE: Benchmarking Generalization Via In-Context Instructions on 1,600+ Language Tasks
AUTHORS: YIZHONG WANG et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: How can we measure the generalization of models to a variety of unseen tasks when provided with their language instructions? To facilitate progress in this goal, we introduce Natural-Instructions v2, a collection of 1,600+ diverse language tasks and their expert written instructions.

35, TITLE: Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED
AUTHORS: QUZHE HUANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We appeal to future research to take into consideration the issues with the recommend-revise scheme when designing new models and annotation schemes.

36, TITLE: L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models
AUTHORS: Ravindra Nayak ; Raviraj Joshi
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: We also release L3Cube-HingLID Corpus, the largest code-mixed Hindi-English language identification(LID) dataset and HingBERT-LID, a production-quality LID model to facilitate capturing of more code-mixed data using the process outlined in this work.

37, TITLE: Detect Rumors in Microblog Posts for Low-Resource Domains Via Adversarial Contrastive Learning
AUTHORS: HONGZHAN LIN et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this paper, we propose an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced.

38, TITLE: Dynamic Position Encoding for Transformers
AUTHORS: Joyce Zheng ; Mehdi Rezagholizadeh ; Peyman Passban
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: However, such embeddings are fixed after training regardless of the task and the word ordering system of the source or target language. In this paper, we propose a novel architecture with new position embeddings depending on the input text to address this shortcoming by taking the order of target words into consideration.

39, TITLE: LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
AUTHORS: Yupan Huang ; Tengchao Lv ; Lei Cui ; Yutong Lu ; Furu Wei
CATEGORY: cs.CL [cs.CL, cs.CV]
HIGHLIGHT: In this paper, we propose LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified text and image masking.

40, TITLE: GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
AUTHORS: LIBO QIN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: However, existing models solely rely on shared parameters, which can only perform implicit alignment across languages. We present Global--Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming.

41, TITLE: Language Contamination Explains The Cross-lingual Capabilities of English Pretrained Models
AUTHORS: Terra Blevins ; Luke Zettlemoyer
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: English pretrained language models, which make up the backbone of many modern NLP systems, require huge amounts of unlabeled training data. These models are generally presented as being trained only on English text but have been found to transfer surprisingly well to other languages.

42, TITLE: Unsupervised Cross-Task Generalization Via Retrieval Augmentation
AUTHORS: Bill Yuchen Lin ; Kangmin Tan ; Chris Miller ; Beiwen Tian ; Xiang Ren
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: Humans can perform unseen tasks by recalling relevant skills that are acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve such cross-task generalization ability of massive multi-task language models such as T0 (Sanh et al., 2021) in an unsupervised setting.

43, TITLE: On The Origin of Hallucinations in Conversational Models: Is It The Datasets or The Models?
AUTHORS: Nouha Dziri ; Sivan Milton ; Mo Yu ; Osmar Zaiane ; Siva Reddy
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallucination. In this work, we investigate the underlying causes of this phenomenon: is hallucination due to the training data, or to the models?

44, TITLE: CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation
AUTHORS: MD AKMAL HAIDAR et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we propose a learning based data augmentation technique tailored for knowledge distillation, called CILDA.

45, TITLE: MoEBERT: from BERT to Mixture-of-Experts Via Importance-Guided Adaptation
AUTHORS: SIMIAO ZUO et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We propose MoEBERT, which uses a Mixture-of-Experts structure to increase model capacity and inference speed.

46, TITLE: HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification
AUTHORS: Pengfei Gao ; Jingpeng Zhao ; Yinglong Ma ; Ahmad Tanvir ; Beihong Jin
CATEGORY: cs.CL [cs.CL, cs.AI, 68T07, I.2.7; I.2.4]
HIGHLIGHT: In this paper, we present a hierarchical and fine-tuning approach based on the Ordered Neural LSTM neural network, abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC.

47, TITLE: ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
AUTHORS: Yu Gu ; Yu Su
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we present ArcaneQA, a novel generation-based model that addresses both the large search space and schema linking in a unified framework with two mutually boosting ingredients: we use dynamic program induction to tackle the large search space and dynamic contextualized encoding to enhance schema linking.

48, TITLE: In-BoXBART: Get Instructions Into Biomedical Multi-Task Learning
AUTHORS: MIHIR PARMAR et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We introduce the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X) various categories. Using this meta-dataset, we propose a unified model termed In-BoXBART, that can jointly learn all tasks of the BoX without any task-specific modules.

49, TITLE: Kpfriends at SemEval-2022 Task 2: NEAMER -- Named Entity Augmented Multi-word Expression Recognizer
AUTHORS: Min Sik Oh
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We present NEAMER -- Named Entity Augmented Multi-word Expression Recognizer.

50, TITLE: What If: Generating Code to Answer Simulation Questions
AUTHORS: Gal Peretz ; Kira Radinsky
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: The dataset is composed of process texts, simulation questions, and their corresponding computer codes represented by the DSL.We propose a neural program synthesis approach based on reinforcement learning with a novel state-transition semantic reward.

51, TITLE: Bridging Cross-Lingual Gaps During Leveraging The Multilingual Sequence-to-Sequence Pretraining for Text Generation
AUTHORS: CHANGTONG ZAN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To bridge the above cross-lingual domain and task gaps, we extend the vanilla pretrain-finetune pipeline with extra code-switching restore task.

52, TITLE: BLISS: Robust Sequence-to-Sequence Learning Via Self-Supervised Input Representation
AUTHORS: ZHENG ZHANG et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we propose a framework-level robust sequence-to-sequence learning approach, named BLISS, via self-supervised input representation, which has the great potential to complement the data-level augmentation approaches.

53, TITLE: A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis
AUTHORS: Bing Wang ; Liang Ding ; Qihuang Zhong ; Ximing Li ; Dacheng Tao
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: However, it is always sensitive to the multi-aspect challenge, where features of multiple aspects in a sentence will affect each other. To mitigate this issue, we design a novel training framework, called Contrastive Cross-Channel Data Augmentation (C3DA).

54, TITLE: Monte Carlo Tree Search for Interpreting Stress in Natural Language
AUTHORS: Kyle Swanson ; Joy Hsu ; Mirac Suzgun
CATEGORY: cs.CL [cs.CL, cs.IT, math.IT]
HIGHLIGHT: In this work, we present a new method for explaining a person's mental state from text using Monte Carlo tree search (MCTS).

55, TITLE: Efficient Reinforcement Learning for Unsupervised Controlled Text Generation
AUTHORS: Bhargav Upadhyay ; Akhilesh Sudhakar ; Arjun Maheswaran
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In contrast, this work proposes a novel approach that provides dense rewards to each generated token.

56, TITLE: Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes
AUTHORS: Kaige Xie ; Sarah Wiegreffe ; Mark Riedl
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we explore one additional utility of the multi-hop decomposition from the perspective of explainable NLP: to create explanation by probing a neural QA model with them.

57, TITLE: Exploring Dimensionality Reduction Techniques in Multilingual Transformers
AUTHORS: Álvaro Huertas-García ; Alejandro Martín ; Javier Huertas-Tato ; David Camacho
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques.

58, TITLE: Zero-shot Entity and Tweet Characterization with Designed Conditional Prompts and Contexts
AUTHORS: SHARATH SRIVATSA et. al.
CATEGORY: cs.CL [cs.CL, cs.IR]
HIGHLIGHT: In this work, we approach the problem of characterizing Named Entities and Tweets as an open-ended text classification and open-ended fact probing problem.We evaluate the zero-shot language model capabilities of Generative Pretrained Transformer 2 (GPT-2) to characterize Entities and Tweets subjectively with human psychology-inspired and logical conditional prefixes and contexts.

59, TITLE: UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language
AUTHORS: David Koleczek ; Alex Scarlatos ; Siddha Karakare ; Preshma Linet Pereira
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Accurately detecting PCL of this form is a difficult task due to limited labeled data and how subtle it can be. In this paper, we describe our system for detecting such language which was submitted to SemEval 2022 Task 4: Patronizing and Condescending Language Detection.

60, TITLE: TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
AUTHORS: Xuanyu Zhang ; Qing Yang ; Dongliang Xu
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we propose a novel transition-based method, TranS, for knowledge graph embedding.

61, TITLE: Nigam@COLIEE-22: Legal Case Retrieval and Entailment Using Cascading of Lexical and Semantic-based Models
AUTHORS: Shubham Kumar Nigam ; Navansh Goel
CATEGORY: cs.CL [cs.CL, cs.IR, cs.LG]
HIGHLIGHT: This paper describes our submission to the Competition on Legal Information Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for tasks 1 and 2.

62, TITLE: COVIBOT: A Smart Chatbot for Assistance and E-Awareness During COVID-19 Pandemic
AUTHORS: MAHA DRISS et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: This work proposes to design and implement a smart chatbot on the theme of COVID-19, called COVIBOT, which will be useful in the context of Saudi Arabia.

63, TITLE: Learning to Adapt Domain Shifts of Moral Values Via Instance Weighting
AUTHORS: Xiaolei Huang ; Alexandra Wormley ; Adam Cohen
CATEGORY: cs.CL [cs.CL, cs.SI]
HIGHLIGHT: In this study, we examine domain shifts of moral values and language usage, quantify the effects of domain shifts on the morality classification task, and propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks.

64, TITLE: STRATA: Word Boundaries & Phoneme Recognition From Continuous Urdu Speech Using Transfer Learning, Attention, & Data Augmentation
AUTHORS: Saad Naeem ; Omer Beg
CATEGORY: cs.CL [cs.CL, cs.SD, eess.AS]
HIGHLIGHT: In this paper, we propose STRATA, a framework for supervised phoneme recognition that overcomes the data scarcity issue for low resource languages using a seq2seq neural architecture integrated with transfer learning, attention mechanism, and data augmentation.

65, TITLE: Spanish Abstract Meaning Representation: Annotation of A General Corpus
AUTHORS: SHIRA WEIN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we present the first sizable, general annotation project for Spanish Abstract Meaning Representation.

66, TITLE: Logical Inference for Counting on Semi-structured Tables
AUTHORS: Tomoya Kurosawa ; Hitomi Yanaka
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To handle a numerical type of inference, we propose a logical inference system for reasoning between semi-structured tables and texts.

67, TITLE: Fairly Accurate: Learning Optimal Accuracy Vs. Fairness Tradeoffs for Hate Speech Detection
AUTHORS: Venelin Kovatchev ; Soumyajit Gupta ; Matthew Lease
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We evaluate our methods on the specific task of hate speech detection, in which prior work has shown lack of group fairness across speakers of different English dialects.

68, TITLE: Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
AUTHORS: Weiyan Shi ; Si Chen ; Chiyuan Zhang ; Ruoxi Jia ; Zhou Yu
CATEGORY: cs.CL [cs.CL, cs.AI, cs.CR]
HIGHLIGHT: In this paper, we propose a simple yet effective just-fine-tune-twice privacy mechanism to first fine-tune on in-domain redacted data and then on in-domain private data, to achieve SDP for large Transformer-based language models.

69, TITLE: Comparison Communication Protocols
AUTHORS: Michael R. Klug
CATEGORY: cs.CC [cs.CC, 68Q11]
HIGHLIGHT: We introduce a restriction of the classical 2-party deterministic communication protocol where Alice and Bob are restricted to using only comparison functions.

70, TITLE: Optimal Coding Theorems in Time-Bounded Kolmogorov Complexity
AUTHORS: Zhenjian Lu ; Igor C. Oliveira ; Marius Zimand
CATEGORY: cs.CC [cs.CC]
HIGHLIGHT: We show a coding theorem for rKt with a factor of $2$.

71, TITLE: Safe Self-Refinement for Transformer-based Domain Adaptation
AUTHORS: Tao Sun ; Cheng Lu ; Tianshuo Zhang ; Haibin Ling
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this paper we propose a novel solution named SSRT (Safe Self-Refinement for Transformer-based domain adaptation), which brings improvement from two aspects.

72, TITLE: The Devil Is in The Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training
AUTHORS: HAO LIU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: The Devil Is in The Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training

73, TITLE: Fast Multi-grid Methods for Minimizing Curvature Energy
AUTHORS: Zhenwei Zhang ; Ke Chen ; Yuping Duan
CATEGORY: cs.CV [cs.CV, cs.NA, eess.IV, math.NA]
HIGHLIGHT: In this paper, we propose fast multi-grid algorithms for minimizing both mean curvature and Gaussian curvature energy functionals without sacrificing the accuracy for efficiency.

74, TITLE: In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking
AUTHORS: Yao Sui ; Guanghui Wang ; Li Zhang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: The paper focuses on a classical tracking model, subspace learning, grounded on the fact that the targets in successive frames are considered to reside in a low-dimensional subspace or manifold due to the similarity in their appearances.

75, TITLE: Dataset for Analyzing Various Gaze Zones and Distracted Behaviors of A Driver
AUTHORS: MOHAMMED SHAIQUR RAHMAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: This article presents a synthetic dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones.

76, TITLE: Learning Compositional Representations for Effective Low-Shot Generalization
AUTHORS: Samarth Mishra ; Pengkai Zhu ; Venkatesh Saligrama
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition.

77, TITLE: Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network
AUTHORS: Yuhang He ; Lin Chen ; Junkun Xie ; Long Chen
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a novel framework to learn 3D point cloud semantics from 2D multi-view image observations containing pose error.

78, TITLE: MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
AUTHORS: THOMAS HAYES et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: The richness ensures we are making progress along the core challenges. To this end, we present a large-scale video-audio-text dataset MUGEN, collected using the open-sourced platform game CoinRun [11].

79, TITLE: NICO++: Towards Better Benchmarking for Domain Generalization
AUTHORS: XINGXUAN ZHANG et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: We propose a large-scale benchmark with extensive labeled domains named NICO++{\ddag} along with more rational evaluation methods for comprehensively evaluating DG algorithms.

80, TITLE: Language-Grounded Indoor 3D Semantic Segmentation in The Wild
AUTHORS: David Rozenberszki ; Or Litany ; Angela Dai
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This large number of class categories also induces a large natural class imbalance, both of which are challenging for existing 3D semantic segmentation methods. To learn more robust 3D features in this context, we propose a language-driven pre-training method to encourage learned 3D features that might have limited training examples to lie close to their pre-trained text embeddings.

81, TITLE: Biometric Verification of Humans By Means of Hand Geometry
AUTHORS: Marcos Faundez-Zanuy
CATEGORY: cs.CV [cs.CV, cs.CR]
HIGHLIGHT: This paper describes a hand geometry biometric identification system.

82, TITLE: Visual Attention Methods in Deep Learning: An In-Depth Survey
AUTHORS: Mohammed Hassanin ; Saeed Anwar ; Ibrahim Radwan ; Fahad S Khan ; Ajmal Mian
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG, eess.IV]
HIGHLIGHT: Note that, besides being demanding in terms of training data and computational resources, transformers only cover a single category in self-attention out of the many categories available. We fill this gap and provide an in-depth survey of 50 attention techniques categorizing them by their most prominent features.

83, TITLE: Towards Lightweight Transformer Via Group-wise Transformation for Vision-and-Language Tasks
AUTHORS: GEN LUO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, compressing Transformer remains as an open problem due to its internal complexity of the layer designs, i.e., Multi-Head Attention (MHA) and Feed-Forward Network (FFN). To address this issue, we introduce Group-wise Transformation towards a universal yet lightweight Transformer for vision-and-language tasks, termed as LW-Transformer.

84, TITLE: Real-world Deep Local Motion Deblurring
AUTHORS: Haoying Li ; Ziran Zhang ; Tingting Jiang ; Peng Luo ; Huajun Feng
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem.

85, TITLE: TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
AUTHORS: SHUOJIA ZOU et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, $>$ 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively.

86, TITLE: Self-Supervised Arbitrary-Scale Point Clouds Upsampling Via Implicit Neural Representation
AUTHORS: WENBO ZHAO et. al.
CATEGORY: cs.CV [cs.CV, cs.GR]
HIGHLIGHT: In this paper, we propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously.

87, TITLE: Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection
AUTHORS: JIAXI WU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD.

88, TITLE: Searching Intrinsic Dimensions of Vision Transformers
AUTHORS: Fanghui Xue ; Biao Yang ; Yingyong Qi ; Jack Xin
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: Some pruning methods have been developed to construct efficient vision transformers, but most of them have considered image classification tasks only. Inspired by these results, we propose SiDT, a method for pruning vision transformer backbones on more complicated vision tasks like object detection, based on the search of transformer dimensions.

89, TITLE: Modality-Balanced Embedding for Video Retrieval
AUTHORS: XUN WANG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.IR, stat.ML]
HIGHLIGHT: We propose MBVR (short for Modality Balanced Video Retrieval) with two key components: manually generated modality-shuffled (MS) samples and a dynamic margin (DM) based on visual relevance.

90, TITLE: An Extendable, Efficient and Effective Transformer-based Object Detector
AUTHORS: HWANJUN SONG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we integrate Vision and Detection Transformers (ViDT) to construct an effective and efficient object detector.

91, TITLE: Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge
AUTHORS: Qun Song ; Zhenyu Yan ; Wenjie Luo ; Rui Tan
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper presents {\em Sardino}, an active and dynamic defense approach that renews the inference ensemble at run time to develop security against the adaptive adversary who tries to exfiltrate the ensemble and construct the corresponding effective adversarial examples.

92, TITLE: Pushing The Performance Limit of Scene Text Recognizer Without Human Annotation
AUTHORS: CAIYUAN ZHENG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly.

93, TITLE: Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis
AUTHORS: Yan Ling ; Jianfei yu ; Rui Xia
CATEGORY: cs.CV [cs.CV, cs.CL, cs.MM]
HIGHLIGHT: However, previous approaches either (i) use separately pre-trained visual and textual models, which ignore the crossmodal alignment or (ii) use vision-language models pre-trained with general pre-training tasks, which are inadequate to identify finegrained aspects, opinions, and their alignments across modalities. To tackle these limitations, we propose a task-specific Vision-Language Pre-training framework for MABSA (VLPMABSA), which is a unified multimodal encoder-decoder architecture for all the pretraining and downstream tasks.

94, TITLE: GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
AUTHORS: YASIRU RANASINGHE et. al.
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: We propose a split architecture and use a pseudo-ground truth for abundances to guide the `unmixing network' (UN) optimization.

95, TITLE: Stress-Testing LiDAR Registration
AUTHORS: Amnon Drory ; Shai Avidan ; Raja Giryes
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets.

96, TITLE: Interactiveness Field in Human-Object Interactions
AUTHORS: XINPENG LIU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we introduce a previously overlooked interactiveness bimodal prior: given an object in an image, after pairing it with the humans, the generated pairs are either mostly non-interactive, or mostly interactive, with the former more frequent than the latter.

97, TITLE: Detecting Deepfakes with Self-Blended Images
AUTHORS: Kaede Shiohara ; Toshihiko Yamasaki
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes.

98, TITLE: End-to-end Dense Video Captioning As Sequence Generation
AUTHORS: Wanrong Zhu ; Bo Pang ; Ashish Thapliyal ; William Yang Wang ; Radu Soricut
CATEGORY: cs.CV [cs.CV, cs.CL]
HIGHLIGHT: In this work, we show how to model the two subtasks of dense video captioning jointly as one sequence generation task, and simultaneously predict the events and the corresponding descriptions.

99, TITLE: Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
AUTHORS: AMAN GOYAL et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Identifying and penalizing such riders is vital in curbing road accidents and improving citizens' safety. With this motivation, we propose an approach for detecting, tracking, and counting motorcycle riding violations in videos taken from a vehicle-mounted dashboard camera.

100, TITLE: Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding
AUTHORS: XUN LONG NG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, existing animal behavior datasets have limitations in multiple aspects, including limited numbers of animal classes, data samples and provided tasks, and also limited variations in environmental conditions and viewpoints. To address these limitations, we create a large and diverse dataset, Animal Kingdom, that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors.

101, TITLE: End-to-end Weakly-supervised Multiple 3D Hand Mesh Reconstruction from Single Image
AUTHORS: Jinwei Ren ; Jianke Zhu ; Jialiang Zhang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from single 2D image.

102, TITLE: CenterNet++ for Object Detection
AUTHORS: KAIWEN DUAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we demonstrate that the bottom-up approaches are as competitive as the top-down and enjoy higher recall.

103, TITLE: Joint Multi-view Unsupervised Feature Selection and Graph Learning
AUTHORS: Si-Guo Fang ; Dong Huang ; Chang-Dong Wang ; Yong Tang
CATEGORY: cs.CV [cs.CV, cs.LG, stat.ML]
HIGHLIGHT: In light of this, this paper presents a joint multi-view unsupervised feature selection and graph learning (JMVFG) approach.

104, TITLE: Unsupervised Domain Adaptation and Super Resolution on Drone Images for Autonomous Dry Herbage Biomass Estimation
AUTHORS: PAUL ALBERT et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner.

105, TITLE: An Adaptive Task-Related Component Analysis Method for SSVEP Recognition
AUTHORS: Vangelis P. Oikonomou
CATEGORY: cs.CV [cs.CV, cs.HC]
HIGHLIGHT: This study develops a new method to learn from limited calibration data and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEPs detection.

106, TITLE: Visio-Linguistic Brain Encoding
AUTHORS: Subba Reddy Oota ; Jashn Arora ; Vijay Rowtula ; Manish Gupta ; Raju S. Bapi
CATEGORY: cs.CV [cs.CV, cs.AI, cs.CL, cs.LG, q-bio.NC]
HIGHLIGHT: In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding.

107, TITLE: The Z-axis, X-axis, Weight and Disambiguation Methods for Constructing Local Reference Frame in 3D Registration: An Evaluation
AUTHORS: BAO ZHAO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we comprehensively analyze the z-axis, x-axis, weight and disambiguation methods in existing LRFs, and obtain six z-axis and eight x-axis, five weight and two disambiguation methods.

108, TITLE: VDTR: Video Deblurring with Transformer
AUTHORS: Mingdeng Cao ; Yanbo Fan ; Yong Zhang ; Jue Wang ; Yujiu Yang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper presents VDTR, an effective Transformer-based model that makes the first attempt to adapt Transformer for video deblurring.

109, TITLE: Deep Learning Based Automatic Detection of Dicentric Chromosome
AUTHORS: Angad Singh Wadhwa ; Nikhil Tyagi ; Pinaki Roy Chowdhury
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper proposes a completely data driven framework which requires minimum intervention of field experts and can be deployed in emergency cases with relative ease.

110, TITLE: Attention Mechanism Based Cognition-level Scene Understanding
AUTHORS: XUEJIAO TANG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we propose a parallel attention-based cognitive VCR network PAVCR, which fuses visual-textual information efficiently and encodes semantic information in parallel to enable the model to capture rich information for cognition-level inference.

111, TITLE: Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion
AUTHORS: EVONNE NG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of corresponding listener motion.

112, TITLE: Learning with Signatures
AUTHORS: J. de Curtò ; I. de Zarzà ; Carlos T. Calafate ; Hong Yan
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work we investigate the use of the Signature Transform in the context of Learning.

113, TITLE: Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation
AUTHORS: GINAM KIM et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper proposes a novel in-vehicle monitoring system the combines 3D pose estimation, seat-belt segmentation, and seat-belt status classification networks.

114, TITLE: DR-GAN: Distribution Regularization for Text-to-Image Generation
AUTHORS: Hongchen Tan ; Xiuping Liu ; Baocai Yin ; Xin Li
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning.

115, TITLE: Neural Space-filling Curves
AUTHORS: Hanyu Wang ; Kamal Gupta ; Larry Davis ; Abhinav Shrivastava
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.

116, TITLE: Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
AUTHORS: Wele Gedara Chaminda Bandara ; Vishal M. Patel
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG, eess.IV]
HIGHLIGHT: In this paper, we propose a simple yet effective way to leverage the information from unlabeled bi-temporal images to improve the performance of CD approaches.

117, TITLE: Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification
AUTHORS: ZHIJUN HU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a Global-Supervised Contrastive loss and a view-aware-based post-processing (VABPP) method for the field of vehicle re-identification.

118, TITLE: OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval
AUTHORS: YUNHAO DU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions.

119, TITLE: Privacy-Preserving Image Classification Using Isotropic Network
AUTHORS: AprilPyone MaungMaung ; Hitoshi Kiya
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: In this paper, we propose a privacy-preserving image classification method that uses encrypted images and an isotropic network such as the vision transformer.

120, TITLE: VSA: Learning Varied-Size Window Attention in Vision Transformers
AUTHORS: Qiming Zhang ; Yufei Xu ; Jing Zhang ; Dacheng Tao
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, current models adopt a hand-crafted fixed-size window design, which restricts their capacity of modeling long-term dependencies and adapting to objects of different sizes. To address this drawback, we propose \textbf{V}aried-\textbf{S}ize Window \textbf{A}ttention (VSA) to learn adaptive window configurations from data.

121, TITLE: Deep Equilibrium Optical Flow Estimation
AUTHORS: Shaojie Bai ; Zhengyang Geng ; Yash Savani ; J. Zico Kolter
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: They can converge poorly and thereby suffer from performance degradation. To combat these drawbacks, we propose deep equilibrium (DEQ) flow estimators, an approach that directly solves for the flow as the infinite-level fixed point of an implicit layer (using any black-box solver), and differentiates through this fixed point analytically (thus requiring $O(1)$ training memory).

122, TITLE: Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
AUTHORS: Feihu Zhang ; Vladlen Koltun ; Philip Torr ; René Ranftl ; Stephan R. Richter
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we introduce contrastive learning for feature alignment in cross-domain adaptation.

123, TITLE: GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation
AUTHORS: SHI GONG et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: We present a novel two-stage Geometry Prior-based Transformation framework named GitNet, consisting of (i) the geometry-guided pre-alignment and (ii) ray-based transformer.

124, TITLE: Efficient Linear Attention for Fast and Accurate Keypoint Matching
AUTHORS: Suwichaya Suwanwimolkul ; Satoshi Komorita
CATEGORY: cs.CV [cs.CV, I.4; I.5]
HIGHLIGHT: To further improve the efficiency, we propose the joint learning of feature matching and description.

125, TITLE: Multiple-environment Self-adaptive Network for Aerial-view Geo-localization
AUTHORS: TINGYU WANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, existing methods meet large performance drops under realistic weather, such as rain and fog, since they do not take the domain shift between the training data and multiple test environments into consideration. To minor this domain gap, we propose a Multiple-environment Self-adaptive Network (MuSe-Net) to dynamically adjust the domain shift caused by environmental changing.

126, TITLE: Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation
AUTHORS: YULEI LU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we argue that the latent space contains more information to be exploited thus taking one step further to capitalize on it.

127, TITLE: Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint
AUTHORS: Jiaxi Wu ; Jiaxin Chen ; Di Huang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Active learning for object detection is more challenging and existing efforts on it are relatively rare. In this paper, we propose a novel hybrid approach to address this problem, where the instance-level uncertainty and diversity are jointly considered in a bottom-up manner.

128, TITLE: Subspace Nonnegative Matrix Factorization for Feature Representation
AUTHORS: JUNHANG LI et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: This paper proposes a new NMF method by introducing adaptive weights to identify key features in the original space so that only a subspace involves generating the new representation.

129, TITLE: Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
AUTHORS: Antonio Tavera ; Edoardo Arnaudo ; Carlo Masone ; Barbara Caputo
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery.

130, TITLE: What Goes Beyond Multi-modal Fusion in One-stage Referring Expression Comprehension: An Empirical Study
AUTHORS: GEN LUO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Most of the existing work in one-stage referring expression comprehension (REC) mainly focuses on multi-modal fusion and reasoning, while the influence of other factors in this task lacks in-depth exploration. To fill this gap, we conduct an empirical study in this paper.

131, TITLE: BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
AUTHORS: ZIWEI LUO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction.

132, TITLE: A Comprehensive Survey on Data-Efficient GANs in Image Generation
AUTHORS: ZIQIANG LI et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we revisit and define DE-GANs from the perspective of distribution optimization.

133, TITLE: MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
AUTHORS: YUANHAO CAI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: These CNN-based methods achieve impressive restoration performance while showing limitations in capturing the long-range dependencies and self-similarity prior. To cope with this problem, we propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction.

134, TITLE: Hierarchical Optimal Transport for Comparing Histopathology Datasets
AUTHORS: Anna Yeaton ; Rahul G. Krishnan ; Rebecca Mieloszyk ; David Alvarez-Melis ; Grace Huynh
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we propose a principled notion of distance between histopathology datasets based on a hierarchical generalization of optimal transport distances.

135, TITLE: MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
AUTHORS: WUJIANG XU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To tackle the above issues, we propose a multimodal hierarchical shot-aware convolutional network, denoted as MHSCNet, to enhance the frame-wise representation via combining the comprehensive available multimodal information.

136, TITLE: Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition
AUTHORS: Yingjie Chen ; Diqi Chen ; Tao Wang ; Yizhou Wang ; Yun Liang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition.

137, TITLE: Video Action Detection: Analysing Limitations and Challenges
AUTHORS: RAJAT MODI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection.

138, TITLE: Deep Unlearning Via Randomized Conditionally Independent Hessians
AUTHORS: Ronak Mehta ; Sourav Pal ; Vikas Singh ; Sathya N. Ravi
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: We use a variant of a new conditional independence coefficient, L-CODEC, to identify a subset of the model parameters with the most semantic overlap on an individual sample level.

139, TITLE: StyleT2F: Generating Human Faces from Textual Description Using StyleGAN2
AUTHORS: Mohamed Shawky Sabae ; Mohamed Ahmed Dardir ; Remonda Talaat Eskarous ; Mohamed Ramzy Ebbed
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this work, we present StyleT2F, a method of controlling the output of StyleGAN2 using text, in order to be able to generate a detailed human face from textual description.

140, TITLE: MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge
AUTHORS: MIRIAM CHA et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper presents the challenge guidelines, datasets, and evaluation metrics for the three sub-challenges.

141, TITLE: Multimodal Few-Shot Object Detection with Meta-Learning Based Cross-Modal Prompting
AUTHORS: GUANGXING HAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.MM]
HIGHLIGHT: We study multimodal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection.

142, TITLE: A Robust and Scalable Attention Guided Deep Learning Framework for Movement Quality Assessment
AUTHORS: Aditya Kanade ; Mansi Sharma ; Manivannan Muniyandi
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: We propose three novel skeletal data augmentation schemes.

143, TITLE: Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
AUTHORS: YUCHAO ZHENG et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.

144, TITLE: Tracking Monocular Camera Pose and Deformation for SLAM Inside The Human Body
AUTHORS: Juan J. Gomez Rodriguez ; J. M. M Montiel ; Juan D. Tardos
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper we propose a novel method to simultaneously track the camera pose and the 3D scene deformation, without any assumption about environment topology or shape.

145, TITLE: Saliency in Augmented Reality
AUTHORS: HUIYU DUAN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Therefore, in this paper, we mainly analyze the interaction effect between background (BG) scenes and AR contents, and study the saliency prediction problem in AR.

146, TITLE: Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning
AUTHORS: Chang-Hwan Son ; Da-Hee Jeong
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Unlike traditional image degradation models (IDM), such as rain removal and superresolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI).

147, TITLE: Temporally Efficient Vision Transformer for Video Instance Segmentation
AUTHORS: SHUSHENG YANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS).

148, TITLE: UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion
AUTHORS: Guancheng Chen ; Junli Lin ; Huabiao Qin
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: However, the former usually suffers from overfitting while building cost volume, and the latter has a limited generalization due to the lack of geometric constraint. To solve these problems, we propose a novel multimodal neural network, namely UAMD-Net, for dense depth completion based on fusion of binocular stereo matching and the weak constrain from the sparse point clouds.

149, TITLE: Event-aided Direct Sparse Odometry
AUTHORS: Javier Hidalgo-Carrió ; Guillermo Gallego ; Davide Scaramuzza
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We introduce EDS, a direct monocular visual odometry using events and frames.

150, TITLE: Multi-Frame Self-Supervised Depth with Transformers
AUTHORS: Vitor Guizilini ; Rares Ambrus ; Dian Chen ; Sergey Zakharov ; Adrien Gaidon
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper we revisit feature matching for self-supervised monocular depth estimation, and propose a novel transformer architecture for cost volume generation.

151, TITLE: Mapping LiDAR and Camera Measurements in A Dual Top-View Grid Representation Tailored for Automated Vehicles
AUTHORS: Sven Richter ; Frank Bieder ; Sascha Wirges ; Christoph Stiller
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras.

152, TITLE: Towards A Deeper Understanding of Skeleton-based Gait Recognition
AUTHORS: Torben Teepe ; Johannes Gilg ; Fabian Herzog ; Stefan Hörmann ; Gerhard Rigoll
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks to an efficient architecture for gait recognition.

153, TITLE: 3D Human Pose Estimation for Free-from and Moving Activities Using WiFi
AUTHORS: Yili Ren ; Jie Yang
CATEGORY: cs.CV [cs.CV, cs.HC]
HIGHLIGHT: This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home.

154, TITLE: Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
AUTHORS: Philipp Flotho ; Cosmas Heiss ; Gabriele Steidl ; Daniel J. Strauss
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a novel approach for local Lagrangian motion magnification of facial micromovements.

155, TITLE: Shape-guided Object Inpainting
AUTHORS: Yu Zeng ; Zhe Lin ; Vishal M. Patel
CATEGORY: cs.CV [cs.CV, cs.MM]
HIGHLIGHT: Therefore, we propose a new data preparation method and a novel Contextual Object Generator (CogNet) for the object inpainting task.

156, TITLE: FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
AUTHORS: SUIYI ZHAO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than SOTA, and 5x faster than SOTA.

157, TITLE: It Is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning By Contrastive Data Collection
AUTHORS: Youssef Mohamed ; Faizan Farooq Khan ; Kilichbek Haydarov ; Mohamed Elhoseiny
CATEGORY: cs.CV [cs.CV, cs.AI, cs.CL, cs.LG]
HIGHLIGHT: We show that collecting new data, in the same way, is not effective in mitigating this emotional bias. To remedy this problem, we propose a contrastive data collection approach to balance ArtEmis with a new complementary dataset such that a pair of similar images have contrasting emotions (one positive and one negative).

158, TITLE: A Psycho-linguistic Analysis of BitChute
AUTHORS: Benjamin D. Horne
CATEGORY: cs.CY [cs.CY, cs.CL, cs.SI]
HIGHLIGHT: The MeLa-BitChute dataset and LIWC metadata described in this paper can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KRD1VS.

159, TITLE: PiouCrypt: Decentralized Lattice-based Method for Visual Symmetric Cryptography
AUTHORS: Navid Abapour ; Mohsen Ebadpour
CATEGORY: cs.CR [cs.CR, cs.CV, 68P25 (Primary) 94A60, 15B52, 06B30, 11Y05, 68P27 (Secondary), E.3; G.1.3; F.2.1; F.2.2; I.4]
HIGHLIGHT: This paper presents a novel algorithm for establishing secure private visual communication.

160, TITLE: Communication Bounds for Convolutional Neural Networks
AUTHORS: Anthony Chen ; James Demmel ; Grace Dinh ; Mason Haberle ; Olga Holtz
CATEGORY: cs.DC [cs.DC, cs.CC, cs.DS]
HIGHLIGHT: In this paper, we present new lower bounds on data movement for mixed precision convolutions in both single-processor and parallel distributed memory models, as well as algorithms that outperform current implementations such as Im2Col.

161, TITLE: A Novel ASIC Design Flow Using Weight-Tunable Binary Neurons As Standard Cells
AUTHORS: Ankit Wagle ; Gian Singh ; Sunil Khatri ; Sarma Vrudhula
CATEGORY: cs.ET [cs.ET, cs.AR, cs.LG, cs.NE]
HIGHLIGHT: In this paper, we describe a design of a mixed signal circuit for a binary neuron (a.k.a perceptron, threshold logic gate) and a methodology for automatically embedding such cells in ASICs.

162, TITLE: Persua: A Visual Interactive System to Enhance The Persuasiveness of Arguments in Online Discussion
AUTHORS: MENG XIA et. al.
CATEGORY: cs.HC [cs.HC, cs.CL, cs.LG]
HIGHLIGHT: In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts.

163, TITLE: Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques
AUTHORS: LIWEI CHAN et. al.
CATEGORY: cs.HC [cs.HC, cs.AI]
HIGHLIGHT: In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process.

164, TITLE: Identifying Ethical Issues in AI Partners in Human-AI Co-Creation
AUTHORS: Jeba Rezwana ; Mary Lou Maher
CATEGORY: cs.HC [cs.HC, cs.AI]
HIGHLIGHT: Later in the paper, we present some potential ethical issues in human-AI co-creation and propose to use participatory design fiction as the research methodology to investigate the ethical issues associated with a co-creative AI that communicates with users.

165, TITLE: Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems
AUTHORS: Jeba Rezwana ; Mary Lou Maher
CATEGORY: cs.HC [cs.HC, cs.AI]
HIGHLIGHT: This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems.

166, TITLE: CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
AUTHORS: Mohammadmehdi Naghiaei ; Hossein A. Rahmani ; Yashar Deldjoo
CATEGORY: cs.IR [cs.IR, cs.AI]
HIGHLIGHT: In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework.

167, TITLE: HRCF: Enhancing Collaborative Filtering Via Hyperbolic Geometric Regularization
AUTHORS: Menglin Yang ; Min Zhou ; Jiahong Liu ; Defu Lian ; Irwin King
CATEGORY: cs.IR [cs.IR, cs.AI]
HIGHLIGHT: Recently, several hyperbolic approaches have been proposed to learn high-quality representations for the users and items.

168, TITLE: Sparsely Activated Mixture-of-Experts Are Robust Multi-Task Learners
AUTHORS: SHASHANK GUPTA et. al.
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: In this work, we study whether sparsely activated Mixture-of-Experts (MoE) improve multi-task learning by specializing some weights for learning shared representations and using the others for learning task-specific information.

169, TITLE: Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
AUTHORS: Kento Nozawa ; Issei Sato
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: To understand the current representation learning, we review evaluation methods of representation learning algorithms and theoretical analyses.

170, TITLE: Self-Aware Personalized Federated Learning
AUTHORS: HUILI CHEN et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: Inspired by Bayesian hierarchical models, we develop a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients' training.

171, TITLE: Approaching Sales Forecasting Using Recurrent Neural Networks and Transformers
AUTHORS: IVÁN VALLÉS-PÉREZ et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, stat.AP]
HIGHLIGHT: We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporaci\'on Favorita data set, published as part of a Kaggle competition.

172, TITLE: Trinary Tools for Continuously Valued Binary Classifiers
AUTHORS: Michael Gleicher ; Xinyi Yu ; Yuheng Chen
CATEGORY: cs.LG [cs.LG, cs.AI, cs.HC]
HIGHLIGHT: Such tasks involve examining classifier results, typically using summary statistics and manual examination of details. In this paper, we provide an interactive visualization approach to support such continuously-valued classifier examination tasks.

173, TITLE: StableMoE: Stable Routing Strategy for Mixture of Experts
AUTHORS: DAMAI DAI et. al.
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: In this paper, we propose StableMoE with two training stages to address the routing fluctuation problem.

174, TITLE: TigerLily: Finding Drug Interactions in Silico with The Graph
AUTHORS: Benedek Rozemberczki
CATEGORY: cs.LG [cs.LG, cs.AI, cs.SI, q-bio.QM]
HIGHLIGHT: In this machine learning task, we want to predict whether two drugs have an adverse interaction. Our framework allows us to solve this highly relevant real-world problem using graph mining techniques in these steps: (a) Using PyTigergraph we create a heterogeneous biological graph of drugs and proteins.

175, TITLE: Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
AUTHORS: Quan Ding ; Shenghua Liu ; Bin Zhou ; Huawei Shen ; Xueqi Cheng
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: Therefore, we propose a reconstruction-based anomaly detection method, MissGAN, iteratively learning to decode and encode naturally smooth time series in coarse segments, and finding out a finer segment from low-dimensional representations based on HMM.

176, TITLE: Semantic Interpretation for Convolutional Neural Networks: What Makes A Cat A Cat?
AUTHORS: Hao Xu ; Yuntian Chen ; Dongxiao Zhang
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CV]
HIGHLIGHT: In this work, we introduce the framework of semantic explainable AI (S-XAI), which utilizes row-centered principal component analysis to obtain the common traits from the best combination of superpixels discovered by a genetic algorithm, and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques.

177, TITLE: The Tree Loss: Improving Generalization with Many Classes
AUTHORS: Yujie Wang ; Mike Izbicki
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: We introduce the tree loss as a drop-in replacement for the cross entropy loss.

178, TITLE: Learning Time-dependent PDE Solver Using Message Passing Graph Neural Networks
AUTHORS: Pourya Pilva ; Ahmad Zareei
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: Here, we introduce a graph neural network approach to finding efficient PDE solvers through learning using message-passing models.

179, TITLE: Self-Similarity Priors: Neural Collages As Differentiable Fractal Representations
AUTHORS: MICHAEL POLI et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CV]
HIGHLIGHT: In this work, we investigate the role of learning in the automated discovery of self-similarity and in its utilization for downstream tasks.

180, TITLE: AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks
AUTHORS: HASSAN ELDEEB et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this study, we present a comprehensive evaluation and comparison of the performance characteristics of six popular AutoML frameworks, namely, Auto-Weka, AutoSKlearn, TPOT, Recipe, ATM, and SmartML across 100 data sets from established AutoML benchmark suites.

181, TITLE: Sources of Irreproducibility in Machine Learning: A Review
AUTHORS: Odd Erik Gundersen ; Kevin Coakley ; Christine Kirkpatrick
CATEGORY: cs.LG [cs.LG, cs.AI, I.2.m; A.1]
HIGHLIGHT: Our objective is to provide an overview of the sources of irreproducibility that are reported in the literature.

182, TITLE: Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
AUTHORS: Di Wu ; Yi He ; Xin Luo
CATEGORY: cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT: However, most existing LFA-based models perform such embeddings on a HiDS matrix directly without exploiting its hidden graph structures, thereby resulting in accuracy loss. To address this issue, this paper proposes a graph-incorporated latent factor analysis (GLFA) model.

183, TITLE: Accurate Detection of Sepsis at ED Triage Using Machine Learning with Clinical Natural Language Processing
AUTHORS: OLEKSANDR IVANOV et. al.
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: The purpose of this study was to determine whether EHR data can be extracted and synthesized with the latest machine learning algorithms (KATE Sepsis) and clinical natural language processing to produce accurate sepsis models, and compare KATE Sepsis performance with existing sepsis screening protocols, such as SIRS and qSOFA.

184, TITLE: Towards Comprehensive Testing on The Robustness of Cooperative Multi-agent Reinforcement Learning
AUTHORS: JUN GUO et. al.
CATEGORY: cs.MA [cs.MA, cs.AI, cs.LG]
HIGHLIGHT: To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms.

185, TITLE: Towards A Stronger Theory for Permutation-based Evolutionary Algorithms
AUTHORS: Benjamin Doerr ; Yassine Ghannane ; Marouane Ibn Brahim
CATEGORY: cs.NE [cs.NE, cs.AI]
HIGHLIGHT: To overcome the lack of permutation-based benchmark problems, we propose a general way to transfer the classic pseudo-Boolean benchmarks into benchmarks defined on sets of permutations.

186, TITLE: SymForce: Symbolic Computation and Code Generation for Robotics
AUTHORS: HAYK MARTIROS et. al.
CATEGORY: cs.RO [cs.RO, cs.CV, cs.SC]
HIGHLIGHT: We introduce novel methods to automatically compute tangent space Jacobians, eliminating the need for bug-prone handwritten derivatives.

187, TITLE: Evaluating The Effectiveness of Corrective Demonstrations and A Low-Cost Sensor for Dexterous Manipulation
AUTHORS: Abhineet Jain ; Jack Kolb ; J. M. Abbess IV ; Harish Ravichandar
CATEGORY: cs.RO [cs.RO, cs.AI, cs.LG]
HIGHLIGHT: One way to address this limitation is to gather additional data that better represents the full operating conditions. In this work, we investigate characteristics of such additional demonstrations and their impact on performance.

188, TITLE: TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
AUTHORS: Min Zhou ; Bisheng Li ; Menglin Yang ; Lujia Pan
CATEGORY: cs.SI [cs.SI, cs.AI]
HIGHLIGHT: On the other hand, the available tree-like benchmark datasets are either simulated, with limited node information, or small in scale. To bridge this gap, we present a new benchmark dataset TeleGraph, a highly sparse and hierarchical telecommunication network associated with rich node attributes, for assessing and fostering the link inference techniques.

189, TITLE: Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction
AUTHORS: LIKANG WU et. al.
CATEGORY: cs.SI [cs.SI, cs.AI, cs.IR]
HIGHLIGHT: To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation.

190, TITLE: Caption Feature Space Regularization for Audio Captioning
AUTHORS: Yiming Zhang ; Hong Yu ; Ruoyi Du ; Zhanyu Ma ; Yuan Dong
CATEGORY: cs.SD [cs.SD, cs.CL, eess.AS]
HIGHLIGHT: However, it leads to a significant variation in the optimization directions and weakens the model stability. To eliminate this negative effect, in this paper, we propose a two-stage framework for audio captioning: (i) in the first stage, via the contrastive learning, we construct a proxy feature space to reduce the distances between captions correlated to the same audio, and (ii) in the second stage, the proxy feature space is utilized as additional supervision to encourage the model to be optimized in the direction that benefits all the correlated captions.

191, TITLE: Turing's Cascade Instability Supports The Coordination of The Mind, Brain, and Behavior
AUTHORS: Damian G. Kelty-Stephen ; Madhur Mangalam
CATEGORY: q-bio.NC [q-bio.NC, cs.AI]
HIGHLIGHT: The time has come to confront Turing's cascading instability, which suggests a geometrical framework driven by power laws and can be studied using multifractal formalism and multiscale probability density function analysis.

192, TITLE: Continual Hippocampus Segmentation with Transformers
AUTHORS: Amin Ranem ; Camila González ; Anirban Mukhopadhyay
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting.

193, TITLE: Automatic Spinal Curvature Measurement on Ultrasound Spine Images Using Faster R-CNN
AUTHORS: ZHICHAO LIU et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: The objectives of this study are to construct a fully automatic framework based on Faster R-CNN for detecting vertebral lamina and to measure the fitting spinal curves from the detected lamina pairs.

194, TITLE: Semi-Supervised Super-Resolution
AUTHORS: Ankur Singh ; Piyush Rai
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: We quantitatively and qualitatively analyze the performance of the proposed model and compare it with completely supervised methods as well as other unsupervised techniques.

195, TITLE: AFSC: Adaptive Fourier Space Compression for Anomaly Detection
AUTHORS: HAOTE XU et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this study, we propose an Adaptive Fourier Space Compression (AFSC) module to distill healthy feature for AD.

196, TITLE: Parallel Network with Channel Attention and Post-Processing for Carotid Arteries Vulnerable Plaque Segmentation in Ultrasound Images
AUTHORS: YANCHAO YUAN et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: This paper proposes an automatic convolutional neural network (CNN) method for plaque segmentation in carotid ultrasound images using a small dataset.

197, TITLE: Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
AUTHORS: ZONGCAI DU et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: We propose a novel sequential attention branch, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details.

198, TITLE: Wound Severity Classification Using Deep Neural Network
AUTHORS: D. M. Anisuzzaman ; Yash Patel ; Jeffrey Niezgoda ; Sandeep Gopalakrishnan ; Zeyun Yu
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red.

199, TITLE: Accelerated MRI With Deep Linear Convolutional Transform Learning
AUTHORS: Hongyi Gu ; Burhaneddin Yaman ; Steen Moeller ; Il Yong Chun ; Mehmet Akçakaya
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG, eess.SP, physics.med-ph]
HIGHLIGHT: In this work, we combine ideas from CS, TL and DL reconstructions to learn deep linear convolutional transforms as part of an algorithm unrolling approach.

200, TITLE: Few-Shot Transfer Learning to Improve Chest X-Ray Pathology Detection Using Limited Triplets
AUTHORS: Ananth Reddy Bhimireddy ; John Lee Burns ; Saptarshi Purkayastha ; Judy Wawira Gichoya
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this paper, we introduce a practical approach to improve the predictions of a pre-trained model through Few-Shot Learning (FSL).

201, TITLE: GHM Wavelet Transform for Deep Image Super Resolution
AUTHORS: Ben Lowe ; Hadi Salman ; Justin Zhan
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this work, 37 single-level wavelets are experimentally analyzed from Haar, Daubechies, Biorthogonal, Reverse Biorthogonal, Coiflets, and Symlets wavelet families.

202, TITLE: $?$-Net: A Spatiospectral Network for Retinal OCT Segmentation
AUTHORS: Azade Farshad ; Yousef Yeganeh ; Peter Gehlbach ; Nassir Navab
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In this work, we present $\Upsilon$-Net, an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images.

203, TITLE: Multi-organ Segmentation Network with Adversarial Performance Validator
AUTHORS: Haoyu Fang ; Yi Fang ; Xiaofeng Yang
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: This paper introduces an adversarial performance validation network into a 2D-to-3D segmentation framework.