2023.07.19.txt

1, TITLE: Development of The ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines
AUTHORS: GIOVANNI E. CACCIAMANI et. al.
CATEGORY: cs.AI [cs.AI, cs.CY]
HIGHLIGHT: A few publishers and journals have recently created their own sets of rules; however, the absence of a unified approach may lead to a 'Babel Tower Effect,' potentially resulting in confusion rather than desired standardization. In response to this, we present the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative, with the aim of fostering a cross-disciplinary global inclusive consensus on the ethical use, disclosure, and proper reporting of GAI/GPT/LLM technologies in academia.

2, TITLE: Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education
AUTHORS: Neel Kanwal ; Emiel A. M. Janssen ; Kjersti Engan
CATEGORY: cs.AI [cs.AI, cs.CE, cs.CR]
HIGHLIGHT: In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial Intelligence (AI) algorithms for Digital Pathology (DP).

3, TITLE: Reflections from The Workshop on AI-Assisted Decision Making for Conservation
AUTHORS: LILY XU et. al.
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.

4, TITLE: Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints
AUTHORS: Felix Ulrich-Oltean ; Peter Nightingale ; James Alfred Walker
CATEGORY: cs.AI [cs.AI, cs.LG, cs.LO]
HIGHLIGHT: We explore the problem of selecting encodings for pseudo-Boolean and linear constraints using a supervised machine learning approach.

5, TITLE: Human Body Digital Twin: A Master Plan
AUTHORS: Chenyu Tang ; Shuo Gao ; Luigi G. Occhipinti
CATEGORY: cs.AI [cs.AI, eess.SP]
HIGHLIGHT: The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors. This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development.

6, TITLE: Company2Vec -- German Company Embeddings Based on Corporate Websites
AUTHORS: Christopher Gerling
CATEGORY: cs.AI [cs.AI, q-fin.CP, q-fin.PM]
HIGHLIGHT: With Company2Vec, the paper proposes a novel application in representation learning.

7, TITLE: Local Minima Drive Communications in Cooperative Interaction
AUTHORS: Roger K. Moore
CATEGORY: cs.AI [cs.AI, cs.MA, cs.RO]
HIGHLIGHT: Local Minima Drive Communications in Cooperative Interaction

8, TITLE: Ord2Seq: Regard Ordinal Regression As Label Sequence Prediction
AUTHORS: JINHONG WANG et. al.
CATEGORY: cs.AI [cs.AI, cs.CV]
HIGHLIGHT: In this paper, we propose a simple sequence prediction framework for ordinal regression called Ord2Seq, which, for the first time, transforms each ordinal category label into a special label sequence and thus regards an ordinal regression task as a sequence prediction process.

9, TITLE: ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate Via Parameter Constraint
AUTHORS: ZHENHAO JIANG et. al.
CATEGORY: cs.AI [cs.AI, cs.IR]
HIGHLIGHT: We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.

10, TITLE: GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution
AUTHORS: Yining Lu ; Haoping Yu ; Daniel Khashabi
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations.

11, TITLE: Operator Guidance Informed By AI-Augmented Simulations
AUTHORS: Samuel J. Edwards ; Michael Levine
CATEGORY: cs.AI [cs.AI, cs.LG, physics.ao-ph, stat.AP, 68T07, J.2]
HIGHLIGHT: This paper will present a multi-fidelity, data-adaptive approach with a Long Short-Term Memory (LSTM) neural network to estimate ship response statistics in bimodal, bidirectional seas.

12, TITLE: Autoregressive Diffusion Model for Graph Generation
AUTHORS: LINGKAI KONG et. al.
CATEGORY: cs.AI [cs.AI, cs.LG]
HIGHLIGHT: We propose an \emph{autoregressive diffusion} model for graph generation.

13, TITLE: Machine Learning for SAT: Restricted Heuristics and New Graph Representations
AUTHORS: Mikhail Shirokikh ; Ilya Shenbin ; Anton Alekseev ; Sergey Nikolenko
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Such heuristics can be improved with machine learning (ML) models; they can reduce the number of steps but usually hinder the running time because useful models are relatively large and slow. We suggest the strategy of making a few initial steps with a trained ML model and then releasing control to classical heuristics; this simplifies cold start for SAT solving and can decrease both the number of steps and overall runtime, but requires a separate decision of when to release control to the solver.

14, TITLE: Emotional Intelligence of Large Language Models
AUTHORS: Xuena Wang ; Xueting Li ; Zi Yin ; Yue Wu ; Liu Jia
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence.

15, TITLE: IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit Based on Analyses of Interestingness
AUTHORS: Pedro Sequeira ; Melinda Gervasio
CATEGORY: cs.AI [cs.AI, cs.HC, cs.LG]
HIGHLIGHT: Towards more explainable Deep RL (xDRL), we propose a new framework based on analyses of interestingness.

16, TITLE: Multimodal Machine Learning for Extraction of Theorems and Proofs in The Scientific Literature
AUTHORS: Shrey Mishra ; Antoine Gauquier ; Pierre Senellart
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this paper we propose a multimodal machine learning approach for extraction of theorem-like environments and proofs, based on late fusion of features extracted by individual unimodal classifiers, taking into account the sequential succession of blocks in the document.

17, TITLE: REX: Rapid Exploration and EXploitation for AI Agents
AUTHORS: RITHESH MURTHY et. al.
CATEGORY: cs.AI [cs.AI, cs.LG]
HIGHLIGHT: In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX.

18, TITLE: Biomaker CA: A Biome Maker Project Using Cellular Automata
AUTHORS: Ettore Randazzo ; Alexander Mordvintsev
CATEGORY: cs.AI [cs.AI, cs.LG, cs.NE]
HIGHLIGHT: We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA).

19, TITLE: PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation
AUTHORS: YINGCHAOJIE FENG et. al.
CATEGORY: cs.AI [cs.AI, cs.HC]
HIGHLIGHT: This research proposes PromptMagician, a visual analysis system that helps users explore the image results and refine the input prompts.

20, TITLE: AI for The Generation and Testing of Ideas Towards An AI Supported Knowledge Development Environment
AUTHORS: Ted Selker
CATEGORY: cs.AI [cs.AI, H.5.0; I.2.0]
HIGHLIGHT: It proposes one or multiple contextually feasible solutions for a user to contemplate.

21, TITLE: Rumor Detection with Diverse Counterfactual Evidence
AUTHORS: Kaiwei Zhang ; Junchi Yu ; Haichao Shi ; Jian Liang ; Xiao-Yu Zhang
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Moreover, these methods suffer from less robust results as they employ all the propagation patterns for rumor detection. In this paper, we address the above issues with the proposed Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD).

22, TITLE: QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning
AUTHORS: Xiufeng Huang ; Sheng Zhou
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: To improve the performance of multi-agent reinforcement learning under the constraint of wireless resources, we propose a message importance metric and design an importance-aware scheduling policy to effectively exchange messages.

23, TITLE: Attention Over Pre-trained Sentence Embeddings for Long Document Classification
AUTHORS: Amine Abdaoui ; Sourav Dutta
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Several attempts to address this issue were studied, either by reducing the cost of the self-attention computation or by modeling smaller sequences and combining them through a recurrence mechanism or using a new transformer model. In this paper, we suggest to take advantage of pre-trained sentence transformers to start from semantically meaningful embeddings of the individual sentences, and then combine them through a small attention layer that scales linearly with the document length.

24, TITLE: Towards A Neural Era in Dialogue Management for Collaboration: A Literature Survey
AUTHORS: Amogh Mannekote
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: The paper proceeds to analyze a selected set of recent works that apply neural approaches to collaborative dialogue management, spotlighting prevailing trends in the field.

25, TITLE: How Is ChatGPT's Behavior Changing Over Time?
AUTHORS: Lingjiao Chen ; Matei Zaharia ; James Zou
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: We find that the performance and behavior of both GPT-3.5 and GPT-4 can vary greatly over time.

26, TITLE: On The (In)Effectiveness of Large Language Models for Chinese Text Correction
AUTHORS: YINGHUI LI et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: To explore the Chinese processing ability of ChatGPT, we focus on Chinese Text Correction, a fundamental and challenging Chinese NLP task.

27, TITLE: Text Vectorization Via Transformer-based Language Models and N-gram Perplexities
AUTHORS: Mihailo ?kori?
CATEGORY: cs.CL [cs.CL, 68T50]
HIGHLIGHT: As an alternative to scalar perplexity this research proposes a simple algorithm used to calculate vector values based on n-gram perplexities within the input.

28, TITLE: A Mixed Policy to Improve Performance of Language Models on Math Problems
AUTHORS: Gang Chen
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG, 68T10, I.2.6]
HIGHLIGHT: Considering math problems are deterministic, we propose a mixed policy exploration approach to solve math problems with reinforcement learning.

29, TITLE: Large Language Models Perform Diagnostic Reasoning
AUTHORS: Cheng-Kuang Wu ; Wei-Lin Chen ; Hsin-Hsi Chen
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT).

30, TITLE: Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models
AUTHORS: Pranav Narayanan Venkit ; Mukund Srinath ; Shomir Wilson
CATEGORY: cs.CL [cs.CL, cs.AI, cs.CY, cs.LG]
HIGHLIGHT: We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD).

31, TITLE: Pseudo Outlier Exposure for Out-of-Distribution Detection Using Pretrained Transformers
AUTHORS: JAEYOUNG KIM et. al.
CATEGORY: cs.CL [cs.CL, 68T50]
HIGHLIGHT: In this paper, we propose a simple but effective method called Pseudo Outlier Exposure (POE) that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes.

32, TITLE: Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications
AUTHORS: Vishesh Thakur
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Gender bias in artificial intelligence (AI) and natural language processing has garnered significant attention due to its potential impact on societal perceptions and biases. This research paper aims to analyze gender bias in Large Language Models (LLMs) with a focus on multiple comparisons between GPT-2 and GPT-3.5, some prominent language models, to better understand its implications.

33, TITLE: ChatSpot: Bootstrapping Multimodal LLMs Via Precise Referring Instruction Tuning
AUTHORS: LIANG ZHAO et. al.
CATEGORY: cs.CL [cs.CL, cs.CV]
HIGHLIGHT: In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region.

34, TITLE: Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
AUTHORS: GILCHAN PARK et. al.
CATEGORY: cs.CL [cs.CL, cs.LG]
HIGHLIGHT: While existing databases provide curated biological data from literature and other sources, they are often incomplete and their maintenance is labor-intensive, necessitating alternative approaches. In this study, we propose to harness the capabilities of large language models to address these issues by automatically extracting such knowledge from the relevant scientific literature.

35, TITLE: Teach Model to Answer Questions After Comprehending The Document
AUTHORS: Ruiqing Sun ; Ping Jian
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this paper, we propose a two-stage knowledge distillation method that teaches the model to better comprehend the document by dividing the MRC task into two separate stages.

36, TITLE: Llama 2: Open Foundation and Fine-Tuned Chat Models
AUTHORS: HUGO TOUVRON et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.

37, TITLE: Improving Text Semantic Similarity Modeling Through A 3D Siamese Network
AUTHORS: Jianxiang Zang ; Hui Liu
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: Moreover, this limited structure of semantic vectors is akin to a flattened landscape, which restricts the methods that can be applied in downstream modeling, as they can only navigate this flat terrain. To address this issue, we propose a novel 3D Siamese network for text semantic similarity modeling, which maps semantic information to a higher-dimensional space.

38, TITLE: Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
AUTHORS: Liam Hebert ; Gaurav Sahu ; Nanda Kishore Sreenivas ; Lukasz Golab ; Robin Cohen
CATEGORY: cs.CL [cs.CL, cs.LG, cs.MM, cs.SI]
HIGHLIGHT: We present the Multi-Modal Discussion Transformer (mDT), a novel multi-modal graph-based transformer model for detecting hate speech in online social networks.

39, TITLE: Linearized Relative Positional Encoding
AUTHORS: ZHEN QIN et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we put together a variety of existing linear relative positional encoding approaches under a canonical form and further propose a family of linear relative positional encoding algorithms via unitary transformation.

40, TITLE: Unleashing The Imagination of Text: A Novel Framework for Text-to-image Person Retrieval Via Exploring The Power of Words
AUTHORS: Delong Liu ; Haiwen Li
CATEGORY: cs.CL [cs.CL, cs.AI, cs.CV]
HIGHLIGHT: To fully leverage the expressive power of textual representations, it is essential to accurately map abstract textual descriptions to specific images. To address this issue, we propose a novel framework to Unleash the Imagination of Text (UIT) in text-to-image person retrieval, aiming to fully explore the power of words in sentences.

41, TITLE: Neuromorphic Spintronics Simulated Using An Unconventional Data-driven Thiele Equation Approach
AUTHORS: Anatole Moureaux ; Simon de Wergifosse ; Chloé Chopin ; Flavio Abreu Araujo
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS).

42, TITLE: Face-PAST: Facial Pose Awareness and Style Transfer Networks
AUTHORS: Sunder Ali Khowaja ; Ghulam Mujtaba ; Jiseok Yoon ; Ik Hyun Lee
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a facial pose awareness and style transfer (Face-PAST) network that preserves facial details and structures while generating high-quality stylized images.

43, TITLE: Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles
AUTHORS: Connor Lee ; Jonathan Gustafsson Frennert ; Lu Gan ; Matthew Anderson ; Soon-Jo Chung
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals.

44, TITLE: ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting
AUTHORS: HONGWEI ZHENG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE.

45, TITLE: Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding
AUTHORS: ZEHAN WANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To address the problem that fine-grained annotated data is difficult to obtain, we propose to leverage weakly supervised annotations to learn the 3D visual grounding model, i.e., only coarse scene-sentence correspondences are used to learn object-sentence links.

46, TITLE: Human Action Recognition in Still Images Using ConViT
AUTHORS: Seyed Rohollah Hosseyni ; Hasan Taheri ; Sanaz Seyedin ; Ali Ahmad Rahmani
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Despite the fact that Convolutional Neural Networks (CNNs) have demonstrated impressive results in detecting single objects, they lack the capability to extract the relationship between various regions of an image, which is a crucial factor in human action recognition. To address this problem, this paper proposes a new module that functions like a convolutional layer using Vision Transformer (ViT).

47, TITLE: Arbitrary Point Cloud Upsampling Via Dual Back-Projection Network
AUTHORS: Zhi-Song Liu ; Zijia Wang ; Zhen Jia
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a Dual Back-Projection network for point cloud upsampling (DBPnet).

48, TITLE: EgoVM: Achieving Precise Ego-Localization Using Lightweight Vectorized Maps
AUTHORS: Yuzhe He ; Shuang Liang ; Xiaofei Rui ; Chengying Cai ; Guowei Wan
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps.

49, TITLE: What Can Simple Arithmetic Operations Do for Temporal Modeling?
AUTHORS: WENHAO WU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we explore the potential of four simple arithmetic operations for temporal modeling.

50, TITLE: UPSCALE: Unconstrained Channel Pruning
AUTHORS: ALVIN WAN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In response, our insight is to reorder channels at export time, (1) reducing latency by reducing memory copies and (2) improving accuracy by removing constraints.

51, TITLE: Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond
AUTHORS: Yang Zhao ; Tingbo Hou ; Yu-Chuan Su ; Xuhui Jia. Yandong Li ; Matthias Grundmann
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To achieve authentic restoration, we propose $\textbf{IDM}$, an $\textbf{I}$teratively learned face restoration system based on denoising $\textbf{D}$iffusion $\textbf{M}$odels (DDMs).

52, TITLE: A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
AUTHORS: Chaoyang Zhu ; Long Chen
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this survey, we provide a comprehensive review on the past and recent development of OVD and OVS.

53, TITLE: Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement
AUTHORS: HUAKE WANG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration.

54, TITLE: Revisiting Latent Space of GAN Inversion for Real Image Editing
AUTHORS: Kai Katsumata ; Duc Minh Vo ; Bei Liu ; Hideki Nakayama
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this study, we revisit StyleGANs' hyperspherical prior $\mathcal{Z}$ and combine it with highly capable latent spaces to build combined spaces that faithfully invert real images while maintaining the quality of edited images.

55, TITLE: Video-Mined Task Graphs for Keystep Recognition in Instructional Videos
AUTHORS: Kumar Ashutosh ; Santhosh Kumar Ramakrishnan ; Triantafyllos Afouras ; Kristen Grauman
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Prior work largely treats keystep recognition in isolation of this broader structure, or else rigidly confines keysteps to align with a predefined sequential script. We propose discovering a task graph automatically from how-to videos to represent probabilistically how people tend to execute keysteps, and then leverage this graph to regularize keystep recognition in novel videos.

56, TITLE: Occlusion Aware Student Emotion Recognition Based on Facial Action Unit Detection
AUTHORS: Shrouk Wally ; Ahmed Elsayed ; Islam Alkabbany ; Asem Ali ; Aly Farag
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Given that approximately half of science, technology, engineering, and mathematics (STEM) undergraduate students in U.S. colleges and universities leave by the end of the first year [15], it is crucial to improve the quality of classroom environments. This study focuses on monitoring students' emotions in the classroom as an indicator of their engagement and proposes an approach to address this issue.

57, TITLE: Generation of High Spatial Resolution Terrestrial Surface from Low Spatial Resolution Elevation Contour Maps Via Hierarchical Computation of Median Elevation Regions
AUTHORS: Geetika Barman ; B. S. Daya Sagar
CATEGORY: cs.CV [cs.CV, I.4.3, I.4.9, I.4.m]
HIGHLIGHT: We proposed a simple yet effective morphological approach to convert a sparse Digital Elevation Model (DEM) to a dense Digital Elevation Model.

58, TITLE: Accuracy Versus Time Frontiers of Semi-supervised and Self-supervised Learning on Medical Images
AUTHORS: Zhe Huang ; Ruijie Jiang ; Shuchin Aeron ; Michael C. Hughes
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: This study contributes a carefully-designed benchmark to help answer a practitioner's key question: given a small labeled dataset and a limited budget of hours to spend on training, what gains from additional unlabeled images are possible and which methods best achieve them?

59, TITLE: Fusing Hand and Body Skeletons for Human Action Recognition in Assembly
AUTHORS: Dustin Aganian ; Mona Köhler ; Benedict Stephan ; Markus Eisenbach ; Horst-Michael Gross
CATEGORY: cs.CV [cs.CV, cs.LG, cs.RO]
HIGHLIGHT: Although body skeleton approaches are widely used for action recognition, they may not be accurate enough for assembly actions where the worker's fingers and hands play a significant role. To address this limitation, we propose a method in which less detailed body skeletons are combined with highly detailed hand skeletons.

60, TITLE: Towards A Performance Analysis on Pre-trained Visual Question Answering Models for Autonomous Driving
AUTHORS: Kaavya Rekanar ; Ciarán Eising ; Ganesh Sistu ; Martin Hayes
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This short paper presents a preliminary analysis of three popular Visual Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT, in the context of answering questions relating to driving scenarios.

61, TITLE: Visual Validation Versus Visual Estimation: A Study on The Average Value in Scatterplots
AUTHORS: Daniel Braun ; Ashley Suh ; Remco Chang ; Michael Gleicher ; Tatiana von Landesberger
CATEGORY: cs.CV [cs.CV, cs.GR]
HIGHLIGHT: We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data.

62, TITLE: TractCloud: Registration-free Tractography Parcellation with A Novel Local-global Streamline Point Cloud Representation
AUTHORS: TENGFEI XUE et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose TractCloud, a registration-free framework that performs whole-brain tractography parcellation directly in individual subject space.

63, TITLE: Harnessing The Power of AI Based Image Generation Model DALLE 2 in Agricultural Settings
AUTHORS: Ranjan Sapkota
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: By synergistically utilizing the natural language processing proficiency of chatGPT and the generative prowess of the DALLE 2 model, which employs a Generative Adversarial Networks (GANs) framework, our research offers an innovative method to transform textual descriptors into realistic visual content.

64, TITLE: Disentangle Then Parse:Night-time Semantic Segmentation with Illumination Disentanglement
AUTHORS: ZHIXIANG WEI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Most prior semantic segmentation methods have been developed for day-time scenes, while typically underperforming in night-time scenes due to insufficient and complicated lighting conditions. In this work, we tackle this challenge by proposing a novel night-time semantic segmentation paradigm, i.e., disentangle then parse (DTP).

65, TITLE: A Comparative Analysis of SR-GAN Models
AUTHORS: FATEMEH REZAPOOR NIKROO et. al.
CATEGORY: cs.CV [cs.CV, cs.CL, eess.IV]
HIGHLIGHT: In this study, we evaluate the performance of multiple state-of-the-art SR GAN (Super Resolution Generative Adversarial Network) models, ESRGAN, Real-ESRGAN and EDSR, on a benchmark dataset of real-world images which undergo degradation using a pipeline.

66, TITLE: Light-Weight Vision Transformer with Parallel Local and Global Self-Attention
AUTHORS: Nikolas Ebert ; Laurenz Reichardt ; Didier Stricker ; Oliver Wasenmüller
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We identify computationally expensive blocks in the original PLG-ViT architecture and propose several redesigns aimed at reducing the number of parameters and floating-point operations.

67, TITLE: MOCA: Self-supervised Representation Learning By Predicting Masked Online Codebook Assignments
AUTHORS: SPYROS GIDARIS et. al.
CATEGORY: cs.CV [cs.CV, cs.AI, cs.LG]
HIGHLIGHT: In this work, we propose a single-stage and standalone method, MOCA, which unifies both desired properties using novel mask-and-predict objectives defined with high-level features (instead of pixel-level details).

68, TITLE: Plug The Leaks: Advancing Audio-driven Talking Face Generation By Preventing Unintended Information Flow
AUTHORS: Dogucan Yaman ; Fevziye Irem Eyiokur ; Leonard Bärmann ; Hazim Kemal Ekenel ; Alexander Waibel
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we start by identifying several problematic aspects of synchronization methods in recent audio-driven talking face generation approaches.

69, TITLE: AnyDoor: Zero-shot Object-level Image Customization
AUTHORS: XI CHEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations in a harmonious way.

70, TITLE: CG-fusion CAM: Online Segmentation of Laser-induced Damage on Large-aperture Optics
AUTHORS: YUEYUE HAN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially under-activated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method with Continuous Gradient CAM and its nonlinear multi-scale fusion (CG-fusion CAM).

71, TITLE: Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells
AUTHORS: XINYI YE et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error.

72, TITLE: Similarity Min-Max: Zero-Shot Day-Night Domain Adaptation
AUTHORS: Rundong Luo ; Wenjing Wang ; Wenhan Yang ; Jiaying Liu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper challenges a more complicated scenario with border applicability, i.e., zero-shot day-night domain adaptation, which eliminates reliance on any nighttime data. Unlike prior zero-shot adaptation approaches emphasizing either image-level translation or model-level adaptation, we propose a similarity min-max paradigm that considers them under a unified framework.

73, TITLE: The FathomNet2023 Competition Dataset
AUTHORS: ERIC ORENSTEIN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Creating models that are able to recognize when an image or video sequence contains a new organism, an unusual collection of animals, or is otherwise out-of-sample is critical to fully leverage visual data in the ocean.

74, TITLE: MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection
AUTHORS: Zewei Lin ; Yanqing Shen ; Sanping Zhou ; Shitao Chen ; Nanning Zheng
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection, which integrates both the feature-level fusion and decision-level fusion to fully utilize the information in the image.

75, TITLE: OPHAvatars: One-shot Photo-realistic Head Avatars
AUTHORS: Shaoxu Li
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose a method for synthesizing photo-realistic digital avatars from only one portrait as the reference.

76, TITLE: Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation
AUTHORS: FEDERICO BETTI et. al.
CATEGORY: cs.CV [cs.CV, cs.CL]
HIGHLIGHT: We introduce a novel automated method for Visual Concept Evaluation (ViCE), i.e. to assess consistency between a generated/edited image and the corresponding prompt/instructions, with a process inspired by the human cognitive behaviour.

77, TITLE: GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
AUTHORS: ZHUOLING LI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we propose a simple, fast, and end-to-end detector that still maintains high detection precision.

78, TITLE: Class-relation Knowledge Distillation for Novel Class Discovery
AUTHORS: Peiyan Gu ; Chuyu Zhang ; Ruijie Xu ; Xuming He
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Empirically, we find that such class relation becomes less informative during typical discovery training. To prevent such information loss, we propose a novel knowledge distillation framework, which utilizes our class-relation representation to regularize the learning of novel classes.

79, TITLE: SphereNet: Learning A Noise-Robust and General Descriptor for Point Cloud Registration
AUTHORS: Guiyu Zhao ; Zhentao Guo ; Xin Wang ; Hongbin Ma
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration.

80, TITLE: Augmenting CLIP with Improved Visio-Linguistic Reasoning
AUTHORS: Samyadeep Basu ; Maziar Sanjabi ; Daniela Massiceti ; Shell Xu Hu ; Soheil Feizi
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, these contrastively trained vision-language models often fail on compositional visio-linguistic tasks such as Winoground with performance equivalent to random chance. In our paper, we address this issue and propose a sample-efficient light-weight method called SDS-CLIP to improve the compositional visio-linguistic reasoning capabilities of CLIP.

81, TITLE: Measuring Student Behavioral Engagement Using Histogram of Actions
AUTHORS: Ahmed Abdelkawy ; Islam Alkabbany ; Asem Ali ; Aly Farag
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: In this paper, we propose a novel technique for measuring behavioral engagement through students' actions recognition.

82, TITLE: OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation
AUTHORS: Dongming Wu ; Tiancai Wang ; Yuang Zhang ; Xiangyu Zhang ; Jianbing Shen
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we break up the previous offline belief and propose a simple yet effective online model using explicit query propagation, named OnlineRefer.

83, TITLE: LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition Under Label Noise
AUTHORS: Zhiyu Wu ; Jinshi Cui
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: The derived noisy labels significantly harm the performance in real-world scenarios. To address this issue, we present a new FER model named Landmark-Aware Net~(LA-Net), which leverages facial landmarks to mitigate the impact of label noise from two perspectives.

84, TITLE: NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
AUTHORS: Stefan Lionar ; Xiangyu Xu ; Min Lin ; Gim Hee Lee
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: However, we identified two key limitations of MCC: 1) The Transformer decoder is inefficient in handling large number of query points; 2) The 3D representation struggles to recover high-fidelity details. In this paper, we propose a new approach called NU-MCC that addresses these limitations.

85, TITLE: Knowledge Distillation for Object Detection: from Generic to Remote Sensing Datasets
AUTHORS: Hoàng-Ân Lê ; Minh-Tan Pham
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we evaluate in a remote sensing context various off-the-shelf object detection knowledge distillation methods which have been originally developed on generic computer vision datasets such as Pascal VOC.

86, TITLE: You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
AUTHORS: JINGHAN SUN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we propose a co-evolutionary image and report distillation (CEIRD) framework, which approaches semi-supervised abnormality detection in CXR by grounding the visual detection results with text-classified abnormalities from paired radiology reports, and vice versa.

87, TITLE: Pixel-wise Graph Attention Networks for Person Re-identification
AUTHORS: Wenyu Zhang ; Qing Ding ; Jian Hu ; Yi Ma ; Mingzhe Lu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we explore the application of graph attention networks (GAT) in image feature extraction.

88, TITLE: PixelHuman: Animatable Neural Radiance Fields from Few Images
AUTHORS: Gyumin Shim ; Jaeseong Lee ; Junha Hyung ; Jaegul Choo
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose PixelHuman, a novel human rendering model that generates animatable human scenes from a few images of a person with unseen identity, views, and poses.

89, TITLE: MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
AUTHORS: YUKI KONDO et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: The detail of the challenge with the SOD4SB dataset is introduced in this paper.

90, TITLE: LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
AUTHORS: SAMBIT MOHAPATRA et. al.
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: We present a real-time multi-task convolutional neural network for LiDAR-based object detection, semantics, and motion segmentation.

91, TITLE: A Survey on Multi-Objective Neural Architecture Search
AUTHORS: Seyed Mahdi Shariatzadeh ; Mahmood Fathy ; Reza Berangi ; Mohammad Shahverdy
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we present an overview of principal and state-of-the-art works in the field of MONAS.

92, TITLE: PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images Via Secure Flow
AUTHORS: LIN YUAN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper proposes a novel paradigm for facial privacy protection that unifies multiple characteristics including anonymity, diversity, reversibility and security within a single lightweight framework.

93, TITLE: In Defense of Clip-based Video Relation Detection
AUTHORS: Meng Wei ; Long Chen ; Wei Ji ; Xiaoyu Yue ; Roger Zimmermann
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a Hierarchical Context Model (HCM) that enriches the object-based spatial context and relation-based temporal context based on clips.

94, TITLE: DARTS: Double Attention Reference-based Transformer for Super-resolution
AUTHORS: Masoomeh Aslahishahri ; Jordan Ubbens ; Ian Stavness
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We present DARTS, a transformer model for reference-based image super-resolution.

95, TITLE: EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation
AUTHORS: YINGYU CHEN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: However, current methods usually suffer from the drawback that it is difficult to balance the computational cost, estimation accuracy, and theoretical support in a unified framework. To alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence (DST) into semi-supervised medical image segmentation, dubbed Evidential Inference Learning (EVIL).

96, TITLE: Deep Physics-Guided Unrolling Generalization for Compressed Sensing
AUTHORS: Bin Chen ; Jiechong Song ; Jingfen Xie ; Jian Zhang
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: It has attracted growing attention and become the mainstream for inverse imaging tasks. Focusing on the image compressed sensing (CS) problem, we find the intrinsic defect of this emerging paradigm, widely implemented by deep algorithm-unrolled networks, in which more plain iterations involving real physics will bring enormous computation cost and long inference time, hindering their practical application.

97, TITLE: PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks
AUTHORS: Xue-Cheng Tai ; Hao Liu ; Raymond Chan
CATEGORY: cs.CV [cs.CV, 68U10, 94A08]
HIGHLIGHT: In this paper, we study the encoder-decoder-based network architecture from the algorithmic perspective and provide a mathematical explanation.

98, TITLE: Unsupervised Deep Graph Matching Based on Cycle Consistency
AUTHORS: Siddharth Tourani ; Carsten Rother ; Muhammad Haris Khan ; Bogdan Savchynskkyy
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: As the matching and the consistency loss are discrete, their derivatives cannot be straightforwardly used for learning. We address this issue in a principled way by building our method upon the recent results on black-box differentiation of combinatorial solvers.

99, TITLE: RepViT: Revisiting Mobile CNN From ViT Perspective
AUTHORS: Ao Wang ; Hui Chen ; Zijia Lin ; Hengjun Pu ; Guiguang Ding
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this study, we revisit the efficient design of lightweight CNNs and emphasize their potential for mobile devices.

100, TITLE: DropMix: Reducing Class Dependency in Mixed Sample Data Augmentation
AUTHORS: Haeil Lee ; Hansang Lee ; Junmo Kim
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: To reduce class dependency, we propose the DropMix method, which excludes a specific percentage of data from the MSDA computation.

101, TITLE: MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
AUTHORS: JIAHUI LIU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We propose MarS3D, a plug-and-play motion-aware module for semantic segmentation on multi-scan 3D point clouds.

102, TITLE: Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
AUTHORS: Jiahe Li ; Jiawei Zhang ; Xiao Bai ; Jun Zhou ; Lin Gu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size.

103, TITLE: EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting
AUTHORS: Inhwan Bae ; Jean Oh ; Hae-Gon Jeon
CATEGORY: cs.CV [cs.CV, cs.LG, cs.RO]
HIGHLIGHT: In this paper, we present EigenTrajectory ($\mathbb{ET}$), a trajectory prediction approach that uses a novel trajectory descriptor to form a compact space, known here as $\mathbb{ET}$ space, in place of Euclidean space, for representing pedestrian movements.

104, TITLE: Regression-free Blind Image Quality Assessment
AUTHORS: Xiaoqi Wang ; Jian Xiong ; Hao Gao ; Weisi Lin
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: Regression-based blind image quality assessment (IQA) models are susceptible to biased training samples, leading to a biased estimation of model parameters. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is founded upon retrieving similar instances by incorporating semantic and distortion features.

105, TITLE: PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
AUTHORS: MIAOGE LI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap.

106, TITLE: Learning Adaptive Neighborhoods for Graph Neural Networks
AUTHORS: Avishkar Saha ; Oscar Mendez ; Chris Russell ; Richard Bowden
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size.

107, TITLE: Modular Neural Network Approaches for Surgical Image Recognition
AUTHORS: NOSSEIBA BEN SALEM et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In the second part, we present our approach for data labeling and segmentation with self-training applied on shoulder arthroscopy images.

108, TITLE: Connections Between Operator-splitting Methods and Deep Neural Networks with Applications in Image Segmentation
AUTHORS: Hao Liu ; Xue-Cheng Tai ; Raymond Chan
CATEGORY: cs.CV [cs.CV, 68U10, 94A08]
HIGHLIGHT: In this paper, we give an algorithmic explanation for deep neural networks, especially in their connection with operator splitting and multigrid methods.

109, TITLE: R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut
AUTHORS: YINGJIE NIU et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: This paper presents a novel method to enhance the explainability of Transformer-based image classification models.

110, TITLE: Experimental Security Analysis of DNN-based Adaptive Cruise Control Under Context-Aware Perception Attacks
AUTHORS: XUGUI ZHOU et. al.
CATEGORY: cs.CR [cs.CR, cs.CV, cs.LG]
HIGHLIGHT: We present a combined knowledge-and-data-driven approach to design a context-aware strategy for the selection of the most critical times for triggering the attacks and a novel optimization-based method for the adaptive generation of image perturbations at run-time.

111, TITLE: FedDefender: Client-Side Attack-Tolerant Federated Learning
AUTHORS: SUNGWON PARK et. al.
CATEGORY: cs.CR [cs.CR, cs.AI]
HIGHLIGHT: In this paper, we propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models and avoid the adverse impact of malicious model updates from attackers, even when a server-side defense cannot identify or remove adversaries.

112, TITLE: No Distributed Quantum Advantage for Approximate Graph Coloring
AUTHORS: XAVIER COITEUX-ROY et. al.
CATEGORY: cs.DC [cs.DC, cs.CC, cs.DM, cs.ET]
HIGHLIGHT: We give a new distributed algorithm that finds a $c$-coloring in $\chi$-chromatic graphs in $\tilde{\mathcal{O}}(n^{\frac{1}{\alpha}})$ rounds, with $\alpha = \bigl\lceil\frac{c-1}{\chi - 1}\bigr\rceil$.

113, TITLE: Cloud-native RStudio on Kubernetes for Hopsworks
AUTHORS: Gibson Chikafa ; Sina Sheikholeslami ; Salman Niazi ; Jim Dowling ; Vladimir Vlassov
CATEGORY: cs.DC [cs.DC, cs.AI, cs.SE]
HIGHLIGHT: We present the design and implementation of a multi-user distributed system on Hopsworks, a data-intensive AI platform, following the multi-tenant model that provides RStudio as Software as a Service (SaaS).

114, TITLE: FACTS: Facial Animation Creation Using The Transfer of Styles
AUTHORS: Jack Saunders ; Steven Caulkin ; Vinay Namboodiri
CATEGORY: cs.GR [cs.GR, cs.CV]
HIGHLIGHT: In this paper, we present a novel approach to facial animation by taking existing animations and allowing for the modification of style characteristics.

115, TITLE: PromptCrafter: Crafting Text-to-Image Prompt Through Mixed-Initiative Dialogue with LLM
AUTHORS: Seungho Baek ; Hyerin Im ; Jiseung Ryu ; Juhyeong Park ; Takyeon Lee
CATEGORY: cs.HC [cs.HC, cs.AI]
HIGHLIGHT: In this paper, we present PromptCrafter, a novel mixed-initiative system that allows step-by-step crafting of text-to-image prompt.

116, TITLE: Siamese Networks for Weakly Supervised Human Activity Recognition
AUTHORS: Taoran Sheng ; Manfred Huber
CATEGORY: cs.HC [cs.HC, cs.AI, cs.LG]
HIGHLIGHT: In this paper, we present a model with multiple siamese networks that are trained by using only the information about the similarity between pairs of data samples without knowing the explicit labels.

117, TITLE: Zero-shot Query Reformulation for Conversational Search
AUTHORS: Dayu Yang ; Yue Zhang ; Hui Fang
CATEGORY: cs.IR [cs.IR, cs.CL]
HIGHLIGHT: Nevertheless, existing zero-shot methods face three primary limitations: they are not universally applicable to all retrievers, their effectiveness lacks sufficient explainability, and they struggle to resolve common conversational ambiguities caused by omission. To address these limitations, we introduce a novel Zero-shot Query Reformulation (ZeQR) framework that reformulates queries based on previous dialogue contexts without requiring supervision from conversational search data.

118, TITLE: Jean-Luc Picard at Touché 2023: Comparing Image Generation, Stance Detection and Feature Matching for Image Retrieval for Arguments
AUTHORS: Max Moebius ; Maximilian Enderling ; Sarah T. Bachinger
CATEGORY: cs.IR [cs.IR, cs.CV, H.3.3]
HIGHLIGHT: Participating in the shared task "Image Retrieval for arguments", we used different pipelines for image retrieval containing Image Generation, Stance Detection, Preselection and Feature Matching.

119, TITLE: UniTabE: Pretraining A Unified Tabular Encoder for Heterogeneous Tabular Data
AUTHORS: Yazheng Yang ; Yuqi Wang ; Guang Liu ; Ledell Wu ; Qi Liu
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL]
HIGHLIGHT: The primary research questions underpinning this work revolve around the adaptation to heterogeneous table structures, the establishment of a universal pretraining protocol for tabular data, the generalizability and transferability of learned knowledge across tasks, the adaptation to diverse downstream applications, and the incorporation of incremental columns over time. In response to these challenges, we introduce UniTabE, a pioneering method designed to process tables in a uniform manner, devoid of constraints imposed by specific table structures.

120, TITLE: PAC Neural Prediction Set Learning to Quantify The Uncertainty of Generative Language Models
AUTHORS: Sangdon Park ; Taesoo Kim
CATEGORY: cs.LG [cs.LG, cs.CL, stat.ML]
HIGHLIGHT: In this paper, we propose to learn neural prediction set models that comes with the probably approximately correct (PAC) guarantee for quantifying the uncertainty of GLMs.

121, TITLE: Unsupervised Conditional Slot Attention for Object Centric Learning
AUTHORS: Avinash Kori ; Francesco Locatello ; Francesca Toni ; Ben Glocker
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CV]
HIGHLIGHT: Recent object-centric representation methods like Slot Attention utilize iterative attention to learn composable representations with dynamic inference level binding but fail to achieve specialized slot level binding. To address this, in this paper we propose Unsupervised Conditional Slot Attention using a novel Probabilistic Slot Dictionary (PSD).

122, TITLE: Neural Network Pruning As Spectrum Preserving Process
AUTHORS: Shibo Yao ; Dantong Yu ; Ioannis Koutis
CATEGORY: cs.LG [cs.LG, cs.NE]
HIGHLIGHT: In this paper, we identify the close connection between matrix spectrum learning and neural network training for dense and convolutional layers and argue that weight pruning is essentially a matrix sparsification process to preserve the spectrum.

123, TITLE: Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices
AUTHORS: FELIPE M. DIAS et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, eess.SP]
HIGHLIGHT: In this study, we extracted 27 statistical features from the PPG signal for training machine-learning models based on gradient boosting (XGBoost and CatBoost) and Random Forest (RF) algorithms to assess quality of PPG signals that were labeled as good or poor quality.

124, TITLE: Federated Large Language Model: A Position Paper
AUTHORS: Chaochao Chen ; Xiaohua Feng ; Jun Zhou ; Jianwei Yin ; Xiaolin Zheng
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL]
HIGHLIGHT: We propose the concept of federated LLM, which comprises three key components, i.e., federated LLM pre-training, federated LLM fine-tuning, and federated LLM prompt engineering.

125, TITLE: Learning to Sample Tasks for Meta Learning
AUTHORS: JINGYAO WANG et. al.
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: Lastly, the generalization performance of the models are influenced by task divergence, task entropy, and task difficulty. In response to these findings, we propose a novel task sampler called Adaptive Sampler (ASr).

126, TITLE: Towards The Sparseness of Projection Head in Self-Supervised Learning
AUTHORS: ZEEN SONG et. al.
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: Our findings demonstrate that the projection head enhances the quality of representations by performing contrastive loss in a projected subspace. Therefore, we propose an assumption that only a subset of features is necessary when minimizing the contrastive loss of a mini-batch of data.

127, TITLE: Towards Trustworthy Dataset Distillation
AUTHORS: Shijie Ma ; Fei Zhu ; Zhen Cheng ; Xu-Yao Zhang
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: For the first time, we simultaneously consider both issues and propose a novel paradigm called Trustworthy Dataset Distillation (TrustDD).

128, TITLE: Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning
AUTHORS: Patrick Emami ; Xiangyu Zhang ; David Biagioni ; Ahmed S. Zamzam
CATEGORY: cs.LG [cs.LG, cs.AI, cs.MA, cs.SY, eess.SY]
HIGHLIGHT: Learning non-stationary policies is challenging and typically requires sophisticated or inefficient algorithms. Motivated by the prevalence of this control problem in real-world complex systems, we introduce a simple framework for learning non-stationary policies for multi-timescale MARL.

129, TITLE: Towards Sustainable Deep Learning for Multi-Label Classification on NILM
AUTHORS: An?e Pirnat ; Bla? Bertalani? ; Gregor Cerar ; Mihael Mohor?i? ; Carolina Fortuna
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this paper, we introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency.

130, TITLE: An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness
AUTHORS: Jovita Lukasik ; Michael Moeller ; Margret Keuper
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: In this paper, we analyze the ability of common zero-cost proxies to serve as performance predictors for robustness in the popular NAS-Bench-201 search space.

131, TITLE: Mining of Single-Class By Active Learning for Semantic Segmentation
AUTHORS: Hugues Lambert ; Emma Slade
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: Here the Mining of Single-Class by Active Learning (MiSiCAL) paradigm is introduced where an AL policy is constructed through deep reinforcement learning and exploits quantity-accuracy correlations to build datasets on which high-performance models can be trained with regards to specific classes.

132, TITLE: Overthinking The Truth: Understanding How Language Models Process False Demonstrations
AUTHORS: Danny Halawi ; Jean-Stanislas Denain ; Jacob Steinhardt
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL]
HIGHLIGHT: We study harmful imitation through the lens of a model's internal representations, and identify two related phenomena: overthinking and false induction heads.

133, TITLE: Scaling Laws for Imitation Learning in NetHack
AUTHORS: JENS TUYLS et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT: To demonstrate our findings, we focus on the game of NetHack, a challenging environment featuring procedural generation, stochasticity, long-term dependencies, and partial observability.

134, TITLE: U-shaped Transformer: Retain High Frequency Context in Time Series Analysis
AUTHORS: Qingkui Chen ; Yiqin Zhang
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: In this paper, we consider the low-pass characteristics of transformers and try to incorporate the advantages of MLP.

135, TITLE: Adaptive Topological Feature Via Persistent Homology: Filtration Learning for Point Clouds
AUTHORS: Naoki Nishikawa ; Yuichi Ike ; Kenji Yamanishi
CATEGORY: cs.LG [cs.LG, cs.CG, cs.CV]
HIGHLIGHT: In this paper, we propose a framework that learns a filtration adaptively with the use of neural networks.

136, TITLE: Towards Accelerating Benders Decomposition Via Reinforcement Learning Surrogate Models
AUTHORS: STEPHEN MAK et. al.
CATEGORY: cs.LG [cs.LG, cs.AI, math.OC]
HIGHLIGHT: In this paper we propose a method of accelerating BD with the aid of a surrogate model in place of an NP-hard integer master problem.

137, TITLE: DiTTO: Diffusion-inspired Temporal Transformer Operator
AUTHORS: Oded Ovadia ; Eli Turkel ; Adar Kahana ; George Em Karniadakis
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CE, cs.NA, math.NA]
HIGHLIGHT: We propose an operator learning method to solve time-dependent PDEs continuously in time without needing any temporal discretization.

138, TITLE: NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
AUTHORS: Tianxin Wei ; Zeming Guo ; Yifan Chen ; Jingrui He
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: In this paper, we investigate the neural tangent kernel (NTK)--which reveals the gradient descent dynamics of neural networks--of the multilayer perceptrons (MLP) modules in a PLM and propose to coin a lightweight PLM through NTK-approximating MLP fusion.

139, TITLE: Mitigating Label Bias Via Decoupled Confident Learning
AUTHORS: Yunyi Li ; Maria De-Arteaga ; Maytal Saar-Tsechansky
CATEGORY: cs.LG [cs.LG, cs.CL]
HIGHLIGHT: We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias.

140, TITLE: Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
AUTHORS: Yae Jee Cho ; Gauri Joshi ; Dimitrios Dimitriadis
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CV]
HIGHLIGHT: In our work, we propose FedLabel where clients selectively choose the local or global model to pseudo-label their unlabeled data depending on which is more of an expert of the data.

141, TITLE: Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology
AUTHORS: Mehrad Jalolia ; Marzia Cescon
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CE, cs.SY, eess.SY]
HIGHLIGHT: This paper presents a novel multi-agent reinforcement learning (RL) approach for personalized glucose control in individuals with type 1 diabetes (T1D).

142, TITLE: Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
AUTHORS: ARMAN ZHARMAGAMBETOV et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: The training is further challenged by sparse gradients of $\mathbf{g}$, especially for combinatorial solvers. To address these challenges, we propose using a smooth and learnable Landscape Surrogate $M$ as a replacement for $f\circ \mathbf{g}$.

143, TITLE: Conformal Prediction Under Ambiguous Ground Truth
AUTHORS: DAVID STUTZ et. al.
CATEGORY: cs.LG [cs.LG, cs.CV, stat.ME, stat.ML]
HIGHLIGHT: That is, we do not have ``crisp'', definitive ground truth labels and this uncertainty should be taken into account during calibration. In this paper, we develop a conformal prediction framework for such ambiguous ground truth settings which relies on an approximation of the underlying posterior distribution of labels given inputs.

144, TITLE: An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient
AUTHORS: Yudong Luo ; Guiliang Liu ; Pascal Poupart ; Yangchen Pan
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: Recent methods restrict the per-step reward variance as a proxy. We thoroughly examine the limitations of these variance-based methods, such as sensitivity to numerical scale and hindering of policy learning, and propose to use an alternative risk measure, Gini deviation, as a substitute.

145, TITLE: Exploiting Field Dependencies for Learning on Categorical Data
AUTHORS: Zhibin Li ; Piotr Koniusz ; Lu Zhang ; Daniel Edward Pagendam ; Peyman Moghadam
CATEGORY: cs.LG [cs.LG, cs.AI, cs.DB]
HIGHLIGHT: In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies between fields.

146, TITLE: Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach
AUTHORS: Nidhi Vakil ; Hadi Amiri
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL]
HIGHLIGHT: In this paper, we propose a new perspective on curriculum learning by introducing a novel approach that builds on graph complexity formalisms (as difficulty criteria) and model competence during training.

147, TITLE: Polynomial Time and Dependent Types
AUTHORS: Robert Atkey
CATEGORY: cs.LO [cs.LO, cs.PL]
HIGHLIGHT: Our long-term goal is to combine the extensional reasoning of type theory with intensional reasoning about the resources intrinsically consumed by programs.

148, TITLE: Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration
AUTHORS: JOBERTO S. B. MARTINS et. al.
CATEGORY: cs.NI [cs.NI, cs.AI, I.2.1; C.2.1; C.2.3]
HIGHLIGHT: The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks.

149, TITLE: AI-assisted Improved Service Provisioning for Low-latency XR Over 5G NR
AUTHORS: Moyukh Laha ; Dibbendu Roy ; Sourav Dutta ; Goutam Das
CATEGORY: cs.NI [cs.NI, cs.AI, cs.MM]
HIGHLIGHT: This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames.

150, TITLE: Generic Programming with Extensible Data Types; Or, Making Ad Hoc Extensible Data Types Less Ad Hoc
AUTHORS: Alex Hubers ; J. Garrett Morris
CATEGORY: cs.PL [cs.PL]
HIGHLIGHT: We present a novel approach to generic programming over extensible data types.

151, TITLE: Soundly Handling Linearity
AUTHORS: Wenhao Tang ; Daniel Hillerström ; Sam Lindley ; J. Garrett Morris
CATEGORY: cs.PL [cs.PL]
HIGHLIGHT: We propose a novel approach to soundly combining linear types with effect handlers.

152, TITLE: Newtonian Program Analysis of Probabilistic Programs
AUTHORS: Di Wang ; Thomas Reps
CATEGORY: cs.PL [cs.PL]
HIGHLIGHT: This article presents an interprocedural dataflow-analysis framework, called NPA-PMA, for designing and implementing (partially) non-iterative program analyses of probabilistic programs with unstructured control-flow, nondeterminism, and general recursion.

153, TITLE: Exploring Acceptance of Autonomous Vehicle Policies Using KeyBERT and SNA: Targeting Engineering Students
AUTHORS: Jinwoo Ha ; Dongsoo Kim
CATEGORY: cs.SI [cs.SI, cs.AI, cs.CL, cs.RO]
HIGHLIGHT: This study aims to explore user acceptance of Autonomous Vehicle (AV) policies with improved text-mining methods.

154, TITLE: CertPri: Certifiable Prioritization for Deep Neural Networks Via Movement Cost in Feature Space
AUTHORS: Haibin Zheng ; Jinyin Chen ; Haibo Jin
CATEGORY: cs.SE [cs.SE, cs.AI]
HIGHLIGHT: To overcome the challenges, we propose CertPri, a test input prioritization technique designed based on a movement cost perspective of test inputs in DNNs' feature space.

155, TITLE: Approximating Nonlinear Functions with Latent Boundaries in Low-rank Excitatory-inhibitory Spiking Networks
AUTHORS: William F. Podlaski ; Christian K. Machens
CATEGORY: q-bio.NC [q-bio.NC, cs.NE]
HIGHLIGHT: Overall, our work proposes a new perspective on spiking networks that may serve as a starting point for a mechanistic understanding of biological spike-based computation.

156, TITLE: Outlier-Robust Tensor Low-Rank Representation for Data Clustering
AUTHORS: Tong Wu
CATEGORY: stat.ML [stat.ML, cs.CV, cs.LG]
HIGHLIGHT: This paper develops an outlier-robust tensor low-rank representation (OR-TLRR) method for simultaneous outlier detection and tensor data clustering based on the tensor singular value decomposition (t-SVD) algebraic framework.

157, TITLE: SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs
AUTHORS: Yinghao Aaron Li ; Cong Han ; Nima Mesgarani
CATEGORY: eess.AS [eess.AS, cs.AI, cs.SD]
HIGHLIGHT: This paper introduces a new approach, SLMGAN, to leverage SLM representations for discriminative tasks within the generative adversarial network (GAN) framework, specifically for voice conversion.

158, TITLE: Soft-IntroVAE for Continuous Latent Space Image Super-Resolution
AUTHORS: Zhi-Song Liu ; Zijia Wang ; Zhen Jia
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: Inspired by Variational AutoEncoder, we propose a Soft-introVAE for continuous latent space image super-resolution (SVAE-SR).

159, TITLE: Learned Scalable Video Coding For Humans and Machines
AUTHORS: Hadi Hadizadeh ; Ivan V. Baji?
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: To support such applications, a new paradigm for video coding is needed that will facilitate efficient representation and compression of video for both machine and human use in a scalable manner. In this manuscript, we introduce the first end-to-end learnable video codec that supports a machine vision task in its base layer, while its enhancement layer supports input reconstruction for human viewing.

160, TITLE: Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
AUTHORS: HENG LI et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG).

161, TITLE: Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net
AUTHORS: Fangyijie Wang ; Guénolé Silvestre ; Kathleen M. Curran
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: But training a CNN network from scratch is a challenging task, we proposed a Transfer Learning (TL) method.