2023.11.27.txt

1, TITLE: ToddlerDiffusion: Flash Interpretable Controllable Diffusion Model
AUTHORS: ESLAM MOHAMED BAKR et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper introduces ToddlerDiffusion, an interpretable 2D diffusion image-synthesis framework inspired by the human generation system.

2, TITLE: Large Language Models As Automated Aligners for Benchmarking Vision-Language Models
AUTHORS: YUANFENG JI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we address the limitations via Auto-Bench, which delves into exploring LLMs as proficient aligners, measuring the alignment between VLMs and human intelligence and value through automatic data curation and assessment.

3, TITLE: Universal Jailbreak Backdoors from Poisoned Human Feedback
AUTHORS: Javier Rando ; Florian Tram�r
CATEGORY: cs.AI [cs.AI, cs.CL, cs.CR, cs.LG]
HIGHLIGHT: In this paper, we consider a new threat where an attacker poisons the RLHF training data to embed a "jailbreak backdoor" into the model.

4, TITLE: Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams
AUTHORS: Ramon Pires ; Thales Sales Almeida ; Hugo Abonizio ; Rodrigo Nogueira
CATEGORY: cs.CL [cs.CL, cs.AI, cs.LG]
HIGHLIGHT: However, existing studies often overlook questions that require the integration of visual comprehension, thus compromising the full spectrum and complexity inherent in real-world scenarios. To address this gap, we present a comprehensive framework to evaluate language models on entrance exams, which incorporates both textual and visual elements.

5, TITLE: Charting New Territories: Exploring The Geographic and Geospatial Capabilities of Multimodal LLMs
AUTHORS: Jonathan Roberts ; Timo L�ddecke ; Rehan Sheikh ; Kai Han ; Samuel Albanie
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored, despite potential wide-ranging benefits to navigation, environmental research, urban development, and disaster response.

6, TITLE: Paragraph-to-Image Generation with Information-Enriched Diffusion Model
AUTHORS: WEIJIA WU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation.

7, TITLE: Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models
AUTHORS: Francesco Croce ; Matthias Hein
CATEGORY: cs.CV [cs.CV, cs.CR, cs.LG]
HIGHLIGHT: We propose instead to generate prompt-agnostic adversarial attacks by maximizing the $\ell_2$-distance, in the latent space, between the embedding of the original and perturbed images.

8, TITLE: Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
AUTHORS: DI JIN et. al.
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: In this work we investigate data efficiency of modeling human feedback that is in natural language.

9, TITLE: DP-NMT: Scalable Differentially-Private Machine Translation
AUTHORS: TIMOUR IGAMBERDIEV et. al.
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package.

10, TITLE: TVT: Training-Free Vision Transformer Search on Tiny Datasets
AUTHORS: ZIMIAN WEI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, for the first time, we investigate how to search in a training-free manner with the help of teacher models and devise an effective Training-free ViT (TVT) search framework.

11, TITLE: SEGIC: Unleashing The Emergent Correspondence for In-Context Segmentation
AUTHORS: LINGCHEN MENG et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Unlike previous work with ad-hoc or non-end-to-end designs, we propose SEGIC, an end-to-end segment-in-context framework built upon a single vision foundation model (VFM).

12, TITLE: A Metalearned Neural Circuit for Nonparametric Bayesian Inference
AUTHORS: Jake C. Snell ; Gianluca Bencomo ; Thomas L. Griffiths
CATEGORY: cs.LG [cs.LG, cs.NE, stat.ML]
HIGHLIGHT: Nonparametric Bayesian models naturally capture this phenomenon, but have significant practical barriers to widespread adoption, namely implementation complexity and computational inefficiency. To address this, we present a method for extracting the inductive bias from a nonparametric Bayesian model and transferring it to an artificial neural network.

13, TITLE: Efficient Open-world Reinforcement Learning Via Knowledge Distillation and Autonomous Rule Discovery
AUTHORS: Ekaterina Nikonova ; Cheng Xue ; Jochen Renz
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this work, we propose a general framework that is applicable to deep reinforcement learning agents.

14, TITLE: Cosine Similarity Knowledge Distillation for Individual Class Information Transfer
AUTHORS: Gyeongdo Ham ; Seonghak Kim ; Suin Lee ; Jae-Hyeok Lee ; Daeshik Kim
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We anticipate that this approach will offer valuable insights for future research on model compression.

15, TITLE: Towards Concept-based Interpretability of Skin Lesion Diagnosis Using Vision-Language Models
AUTHORS: Cristiano Patr�cio ; Lu�s F. Teixeira ; Jo�o C. Neves
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples.

16, TITLE: Maximizing Discrimination Capability of Knowledge Distillation with Energy-based Score
AUTHORS: Seonghak Kim ; Gyeongdo Ham ; Suin Lee ; Donggon Jang ; Daeshik Kim
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In our approach, we classify the dataset into two categories (i.e., low energy and high energy samples) based on their energy score.

17, TITLE: Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data
AUTHORS: Melodee Montgomery ; Prosenjit Chatterjee ; John Jenkins ; Kaushik Roy
CATEGORY: cs.LG [cs.LG, cs.NE]
HIGHLIGHT: In this research, we use Touch-Analytics datasets, which include 41 subjects and 30 different behavioral features.

18, TITLE: Cooperative Dual Attention for Audio-Visual Speech Enhancement with Facial Cues
AUTHORS: Feixiang Wang ; Shuang Yang ; Shiguang Shan ; Xilin Chen
CATEGORY: cs.CV [cs.CV, cs.SD, eess.AS]
HIGHLIGHT: In this work, we focus on leveraging facial cues beyond the lip region for robust Audio-Visual Speech Enhancement (AVSE).

19, TITLE: Griffon: Spelling Out All Object Locations at Any Granularity with Large Language Models
AUTHORS: YUFEI ZHAN et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: More importantly, we present $\textbf{Griffon}$, a purely LVLM-based baseline, which does not require the introduction of any special tokens, expert models, or additional detection modules.

20, TITLE: StableSSM: Alleviating The Curse of Memory in State-space Models Through Stable Reparameterization
AUTHORS: Shida Wang ; Qianxiao Li
CATEGORY: cs.LG [cs.LG, cs.AI, cs.CL, math.DS]
HIGHLIGHT: In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of parameterization.

21, TITLE: GPT Struct Me: Probing GPT Models on Narrative Entity Extraction
AUTHORS: Hugo Sousa ; Nuno Guimar�es ; Al�pio Jorge ; Ricardo Campos
CATEGORY: cs.CL [cs.CL, cs.AI, cs.IR]
HIGHLIGHT: Such effectiveness raises a pertinent question: Can these models be leveraged for the extraction of structured information? In this work, we address this question by evaluating the capabilities of two state-of-the-art language models -- GPT-3 and GPT-3.5, commonly known as ChatGPT -- in the extraction of narrative entities, namely events, participants, and temporal expressions.

22, TITLE: One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space
AUTHORS: Raghav Addanki ; Chenyang Li ; Zhao Song ; Chiwun Yang
CATEGORY: cs.LG [cs.LG, cs.CL, stat.ML]
HIGHLIGHT: Despite this, storing the Key and Value matrices $K, V \in \mathbb{R}^{n \times d}$ still necessitates $O( n d)$ space, leading to significant memory usage. In response to these challenges, we introduce a new algorithm that only reads one pass of the data in streaming fashion.

23, TITLE: Multi-modal Instance Refinement for Cross-domain Action Recognition
AUTHORS: Yuan Qing ; Naixing Wu ; Shaohua Wan ; Lixin Duan
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: The problem of negative transfer has been explored in cross-domain object detection, while it remains under-explored in cross-domain action recognition. Therefore, we propose a Multi-modal Instance Refinement (MMIR) method to alleviate the negative transfer based on reinforcement learning.

24, TITLE: CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization
AUTHORS: Ruoyu Zhao ; Mingrui Zhu ; Shiyin Dong ; Nannan Wang ; Xinbo Gao
CATEGORY: cs.CV [cs.CV, 68U05, I.3.3]
HIGHLIGHT: We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples.

25, TITLE: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification
AUTHORS: KE CHENG et. al.
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification

26, TITLE: Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction
AUTHORS: TAOFENG XIE et. al.
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this study, we propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion.

27, TITLE: Large Language Models As Topological Structure Enhancers for Text-Attributed Graphs
AUTHORS: Shengyin Sun ; Yuxiang Ren ; Chen Ma ; Xuecang Zhang
CATEGORY: cs.AI [cs.AI, cs.CL, cs.LG]
HIGHLIGHT: In this work, we explore how to leverage the information retrieval and text generation capabilities of LLMs to refine/enhance the topological structure of text-attributed graphs (TAGs) under the node classification setting.

28, TITLE: How to Ensure A Safe Control Strategy? Towards A SRL for Urban Transit Autonomous Operation
AUTHORS: Zicong Zhao
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: Given this drawback, reinforcement learning applied in the safety-critical autonomous operation domain remains challenging without generating a safe control command sequence that avoids overspeed operations. Therefore, a SSA-DRL framework is proposed in this paper for safe intelligent control of urban rail transit autonomous operation trains.

29, TITLE: CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation
AUTHORS: Zhijie Qu ; Juan Li ; Zerui Ma ; Jianqiang Li
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: Existing medical dialogue models are mostly based on BERT and pre-trained on English corpora, but there is a lack of high-performing models on the task of Chinese medical dialogue generation. To solve the above problem, this paper proposes CMed-GPT, which is the GPT pre-training language model based on Chinese medical domain text.

30, TITLE: �FAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution
AUTHORS: Milan Straka
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: We present CorPipe, the winning entry to the CRAC 2023 Shared Task on Multilingual Coreference Resolution.

31, TITLE: Directly Attention Loss Adjusted Prioritized Experience Replay
AUTHORS: Zhuoying Chen ; Huiping Li ; Zhaoxu Wang
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: In this article, an novel off policy reinforcement learning training framework called Directly Attention Loss Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly quantify the changed extent of the shifted distribution through Parallel Self-Attention network, so as to accurately compensate the error.

32, TITLE: Efficient Local Search for Nonlinear Real Arithmetic
AUTHORS: Zhonghan Wang ; Bohua Zhan ; Bohan Li ; Shaowei Cai
CATEGORY: cs.SC [cs.SC]
HIGHLIGHT: In this work, we analyze the difficulties and propose ways to address them, resulting in an efficient search algorithm that covers the full theory of nonlinear real arithmetic.

33, TITLE: RFI Detection with Spiking Neural Networks
AUTHORS: Nicholas J. Pritchard ; Andreas Wicenec ; Mohammed Bennamoun ; Richard Dodson
CATEGORY: astro-ph.IM [astro-ph.IM, cs.NE]
HIGHLIGHT: This study introduces the first application of SNNs to an astronomical data-processing task, specifically RFI detection.

34, TITLE: Electric Vehicles Coordination for Grid Balancing Using Multi-objective Harris Hawks Optimization
AUTHORS: Cristina Bianca Pop ; Tudor Cioara ; Viorica Chifu ; Ionut Anghel ; Francesco Bellesini
CATEGORY: cs.AI [cs.AI, cs.NE, cs.SY, eess.SY]
HIGHLIGHT: In this paper, we propose an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid, by utilizing EVs to store surplus energy and discharge it during periods of energy deficit.

35, TITLE: Prototype of Deployment of Federated Learning with IoT Devices
AUTHORS: Pablo Garc�a Santaclara ; Ana Fern�ndez Vilas ; Rebeca P. D�az Redondo
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: This article presents a prototype of a FL solution where the IoT devices used were raspberry pi boards.

36, TITLE: Ethical Implications of ChatGPT in Higher Education: A Scoping Review
AUTHORS: Ming Li ; Ariunaa Enkhtur ; Fei Cheng ; Beverley Anne Yamamoto
CATEGORY: cs.AI [cs.AI, cs.CY]
HIGHLIGHT: This scoping review explores the ethical challenges of using ChatGPT in education, focusing particularly on issues related to higher education.

37, TITLE: Robust Domain Misinformation Detection Via Multi-modal Feature Alignment
AUTHORS: Hui Liu ; Wenya Wang ; Hao Sun ; Anderson Rocha ; Haoliang Li
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: We propose a novel robust domain and cross-modal approach (\textbf{RDCM}) for multi-modal misinformation detection.

38, TITLE: New Epochs in AI Supervision: Design and Implementation of An Autonomous Radiology AI Monitoring System
AUTHORS: Vasantha Kumar Venugopal ; Abhishek Gupta ; Rohit Takhar ; Vidur Mahajan
CATEGORY: cs.AI [cs.AI, cs.LG]
HIGHLIGHT: With the increasingly widespread adoption of AI in healthcare, maintaining the accuracy and reliability of AI models in clinical practice has become crucial. In this context, we introduce novel methods for monitoring the performance of radiology AI classification models in practice, addressing the challenges of obtaining real-time ground truth for performance monitoring.

39, TITLE: RAISE -- Radiology AI Safety, An End-to-end Lifecycle Approach
AUTHORS: M. JORGE CARDOSO et. al.
CATEGORY: cs.AI [cs.AI, physics.med-ph]
HIGHLIGHT: Beginning with rigorous pre-deployment evaluation and validation, the focus should be on ensuring models meet the highest standards of safety, effectiveness and efficacy for their intended applications.

40, TITLE: Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition
AUTHORS: XIUXIN XIA et. al.
CATEGORY: cs.AI [cs.AI]
HIGHLIGHT: In this paper, an E-nose and olfactory EEG multimodal learning method is proposed for cross-subject olfactory preference recognition.

41, TITLE: Average Token Delay: A Duration-aware Latency Metric for Simultaneous Translation
AUTHORS: Yasumasa Kano ; Katsuhito Sudoh ; Satoshi Nakamura
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we propose a novel latency evaluation metric for simultaneous translation called \emph{Average Token Delay} (ATD) that focuses on the duration of partial translations.

42, TITLE: Analysing The Impact of Removing Infrequent Words on Topic Quality in LDA Models
AUTHORS: Victor Bystrov ; Viktoriia Naboka-Krell ; Anna Staszewska-Bystrova ; Peter Winker
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: Despite popularity of vocabulary pruning, not many guidelines on how to implement it are available in the literature. The aim of the paper is to fill this gap by examining the effects of removing infrequent words for the quality of topics estimated using Latent Dirichlet Allocation.

43, TITLE: Machine Translation for Ge'ez Language
AUTHORS: Aman Kassahun Wassie
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work, we explore various methods to improve Ge'ez MT, including transfer-learning from related languages, optimizing shared vocabulary and token segmentation approaches, finetuning large pre-trained models, and using large language models (LLMs) for few-shot translation with fuzzy matches.

44, TITLE: Controlled Text Generation Via Language Model Arithmetic
AUTHORS: Jasper Dekoninck ; Marc Fischer ; Luca Beurer-Kellner ; Martin Vechev
CATEGORY: cs.CL [cs.CL]
HIGHLIGHT: In this work we introduce model arithmetic, a novel inference framework for composing and biasing LLMs without the need for model (re)training or highly specific datasets.

45, TITLE: Calibrated Language Models Must Hallucinate
AUTHORS: Adam Tauman Kalai ; Santosh S. Vempala
CATEGORY: cs.CL [cs.CL, cs.AI]
HIGHLIGHT: Specifically, if the maximum probability of any fact is bounded, we show that the probability of generating a hallucination is close to the fraction of facts that occur exactly once in the training data (a "Good-Turing" estimate), even assuming ideal training data without errors.

46, TITLE: Evolution of Neural Architectures for Financial Forecasting: A Note on Data Incompatibility During Crisis Periods
AUTHORS: Faizal Hafiz ; Jan Broekaert ; Akshya Swain
CATEGORY: cs.CE [cs.CE, cs.NE]
HIGHLIGHT: This note focuses on the optimization of neural architectures for stock index movement forecasting following a major market disruption or crisis. Given that such crises may introduce a shift in market dynamics, this study aims to investigate whether the training data from market dynamics prior to the crisis are compatible with the data during the crisis period.

47, TITLE: History Filtering in Imperfect Information Games: Algorithms and Complexity
AUTHORS: Christopher Solinas ; Douglas Rebstock ; Nathan R. Sturtevant ; Michael Buro
CATEGORY: cs.GT [cs.GT, cs.AI]
HIGHLIGHT: In this work, we introduce and analyze the computational aspects and tractability of filtering histories for subgame decomposition.

48, TITLE: Image Super-Resolution with Text Prompt Diffusion
AUTHORS: ZHENG CHEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Inspired by advancements in multi-modal methods and text prompt image processing, we introduce text prompts to image SR to provide degradation priors.

49, TITLE: Appearance-based Gaze Estimation Enhanced with Synthetic Images Using Deep Neural Networks
AUTHORS: Dmytro Herashchenko ; Igor Farka?
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: Human eye gaze estimation is an important cognitive ingredient for successful human-robot interaction, enabling the robot to read and predict human behavior. We approach this problem using artificial neural networks and build a modular system estimating gaze from separately cropped eyes, taking advantage of existing well-functioning components for face detection (RetinaFace) and head pose estimation (6DRepNet).

50, TITLE: A Systematic Review of Deep Learning-based Research on Radiology Report Generation
AUTHORS: Chang Liu ; Yuanhe Tian ; Yan Song
CATEGORY: cs.CV [cs.CV, cs.CL]
HIGHLIGHT: In this paper, we present a comprehensive review of deep learning-based RRG from various perspectives.

51, TITLE: A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
AUTHORS: XIANGYU XIONG et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification.

52, TITLE: Continuous Football Player Tracking from Discrete Broadcast Data
AUTHORS: Matthew J. Penn ; Christl A. Donnelly ; Samir Bhatt
CATEGORY: cs.CV [cs.CV, cs.MA]
HIGHLIGHT: Player tracking data remains out of reach for many professional football teams as their video feeds are not sufficiently high quality for computer vision technologies to be used. To help bridge this gap, we present a method that can estimate continuous full-pitch tracking data from discrete data made from broadcast footage.

53, TITLE: Binarized 3D Whole-body Human Mesh Recovery
AUTHORS: ZHITENG LI et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.

54, TITLE: Automated Detection and Counting of Windows Using UAV Imagery Based Remote Sensing
AUTHORS: DHRUV PATEL et. al.
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: In this research, a method to accurately detect and count the number of windows of a building by deploying an Unmanned Aerial Vehicle (UAV) based remote sensing system is proposed.

55, TITLE: One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images
AUTHORS: THOMAS JURRIAANS et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: The focus of this research was to propose and test a novel method for classifying Markush structures.

56, TITLE: GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
AUTHORS: YIWEN CHEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: On the other hand, methods based on implicit 3D representations, like Neural Radiance Field (NeRF), render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges, our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm based on Gaussian Splatting (GS), a novel 3D representation.

57, TITLE: ARIA: On The Interaction Between Architectures, Aggregation Methods and Initializations in Federated Visual Classification
AUTHORS: Vasilis Siomos ; Sergio Naval-Marimont ; Jonathan Passerat-Palmbach ; Giacomo Tarroni
CATEGORY: cs.CV [cs.CV, cs.AI, cs.DC]
HIGHLIGHT: To this end, we conduct the first joint ARchitecture-Initialization-Aggregation study and benchmark ARIAs across a range of medical image classification tasks.

58, TITLE: Stable Cluster Discrimination for Deep Clustering
AUTHORS: Qi Qian
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Stable Cluster Discrimination for Deep Clustering

59, TITLE: Towards Interpretable Classification of Leukocytes Based on Deep Learning
AUTHORS: STEFAN R�HRL et. al.
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: They are supported by machine learning methods, which, despite the lack of labeling and the associated lower contrast, can classify cells with high accuracy where the human observer has little chance to discriminate cells. In order to better integrate these workflows into the clinical decision making process, this work investigates the calibration of confidence estimation for the automated classification of leukocytes.

60, TITLE: Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation
AUTHORS: Paul Engstler ; Luke Melas-Kyriazi ; Christian Rupprecht ; Iro Laina
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we investigate self-supervised representations for instance segmentation without any manual annotations.

61, TITLE: Multi-Class Anomaly Detection Based on Regularized Discriminative Coupled Hypersphere-based Feature Adaptation
AUTHORS: Mehdi Rafiei ; Alexandros Iosifidis
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: This paper introduces a new model by including class discriminative properties obtained by a modified Regularized Discriminative Variational Auto-Encoder (RD-VAE) in the feature extraction process of Coupled-hypersphere-based Feature Adaptation (CFA).

62, TITLE: RSB-Pose: Robust Short-Baseline Binocular 3D Human Pose Estimation with Occlusion Handling
AUTHORS: Xiaoyue Wan ; Zhuo Chen ; Yiming Bao ; Xu Zhao
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To address the first challenge, we propose the Stereo Co-Keypoints Estimation module to improve the view consistency of 2D keypoints and enhance the 3D robustness.

63, TITLE: CRISP: Hybrid Structured Sparsity for Class-aware Model Pruning
AUTHORS: Shivam Aggarwal ; Kuluhan Binici ; Tulika Mitra
CATEGORY: cs.CV [cs.CV, cs.AR, cs.LG]
HIGHLIGHT: In this work, we propose CRISP, a novel pruning framework leveraging a hybrid structured sparsity pattern that combines both fine-grained N:M structured sparsity and coarse-grained block sparsity.

64, TITLE: Segmentation-Based Parametric Painting
AUTHORS: Manuel Ladron de Guevara ; Matthew Fisher ; Aaron Hertzmann
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation.

65, TITLE: Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
AUTHORS: YASSIR BENDOU et. al.
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class.

66, TITLE: GeoViT: A Versatile Vision Transformer Architecture for Geospatial Image Analysis
AUTHORS: Madhav Khirwar ; Ankur Narang
CATEGORY: cs.CV [cs.CV, cs.LG]
HIGHLIGHT: We introduce GeoViT, a compact vision transformer model adept in processing satellite imagery for multimodal segmentation, classification, and regression tasks targeting CO2 and NO2 emissions.

67, TITLE: Highly Detailed and Temporal Consistent Video Stylization Via Synchronized Multi-Frame Diffusion
AUTHORS: Minshan Xie ; Hanyuan Liu ; Chengze Li ; Tien-Tsin Wong
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this paper, we propose a synchronized multi-frame diffusion framework to maintain both the visual details and the temporal consistency.

68, TITLE: MRxaI: Black-Box Explainability for Image Classifiers in A Medical Setting
AUTHORS: Nathan Blake ; Hana Chockler ; David A. Kelly ; Santiago Calderon Pena ; Akchunya Chanchal
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: In this paper we compare the performance of several black-box methods against gradcam on a brain cancer MRI dataset.

69, TITLE: Decouple Content and Motion for Conditional Image-to-Video Generation
AUTHORS: CUIFENG SHEN et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Specifically, we predict temporal motions which include motion vector and residual based on a 3D-UNet diffusion model.

70, TITLE: OneFormer3D: One Transformer for Unified Point Cloud Segmentation
AUTHORS: Maxim Kolodiazhnyi ; Anna Vorontsova ; Anton Konushin ; Danila Rukhovich
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Thereby, the similarity of all segmentation tasks and the implicit relationship between them have not been utilized effectively. This paper presents a unified, simple, and effective model addressing all these tasks jointly.

71, TITLE: ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation
AUTHORS: Yuheng Xue ; Nenglun Chen ; Jun Liu ; Wenyun Sun
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we design a novel pipeline for zero-shot 3D part segmentation, called ZeroPS.

72, TITLE: Bursting Spikes: Efficient and High-performance SNNs for Event-based Vision
AUTHORS: Ziqing Wang ; Yuetong Fang ; Jiahang Cao ; Renjing Xu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: While directly converting the pre-trained artificial neural networks (ANNs) - by replacing the non-linear activation with spiking neurons - can provide SNNs with good performance, the resultant SNNs typically demand long timesteps and high energy consumption to achieve their optimal performance. To address this challenge, we introduce the burst-spike mechanism inspired by the biological nervous system, allowing multiple spikes per timestep to reduce conversion errors and produce low-latency SNNs.

73, TITLE: GCPV: Guided Concept Projection Vectors for The Explainable Inspection of CNN Feature Spaces
AUTHORS: Georgii Mikriukov ; Gesina Schwalbe ; Christian Hellert ; Korinna Bade
CATEGORY: cs.CV [cs.CV, cs.AI]
HIGHLIGHT: Furthermore, current single-layer approaches neglect that information about a concept may be spread over the CNN depth. To overcome these shortcomings, we introduce the local-to-global Guided Concept Projection Vectors (GCPV) approach: It (1) generates local concept vectors that each precisely reconstruct a concept segmentation label, and then (2) generalizes these to global concept and even sub-concept vectors by means of hiearchical clustering.

74, TITLE: MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
AUTHORS: Zhiqi Li ; Yiming Chen ; Lingzhe Zhao ; Peidong Liu
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps.

75, TITLE: HACD: Hand-Aware Conditional Diffusion for Monocular Hand-Held Object Reconstruction
AUTHORS: BOWEN FU et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: In this work, we present Hand-Aware Conditional Diffusion for monocular hand-held object reconstruction (HACD), modeling the hand-object interaction in two aspects.

76, TITLE: A New Benchmark and Model for Challenging Image Manipulation Detection
AUTHORS: Zhenfei Zhang ; Mingyang Li ; Ming-Ching Chang
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: To investigate the State-of-The-Art (SoTA) IMD methods in those challenging conditions, we introduce a new Challenging Image Manipulation Detection (CIMD) benchmark dataset, which consists of two subsets, for evaluating editing-based and compression-based IMD methods, respectively.

77, TITLE: Neural Style Transfer for Computer Games
AUTHORS: Eleftherios Ioannou ; Steve Maddock
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: Here, we present an approach for injecting depth-aware NST as part of the 3D rendering pipeline.

78, TITLE: IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather
AUTHORS: FURQAN AHMED SHAIK et. al.
CATEGORY: cs.CV [cs.CV, cs.RO]
HIGHLIGHT: We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, captured under rain, fog, low light, and snow in unstructured driving conditions.

79, TITLE: Animate124: Animating One Image to 4D Dynamic Scene
AUTHORS: YUYANG ZHAO et. al.
CATEGORY: cs.CV [cs.CV]
HIGHLIGHT: We introduce Animate124 (Animate-one-image-to-4D), the first work to animate a single in-the-wild image into 3D video through textual motion descriptions, an underexplored problem with significant applications.

80, TITLE: ECRF: Entropy-Constrained Neural Radiance Fields Compression with Frequency Domain Optimization
AUTHORS: Soonbin Lee ; Fangwen Shu ; Yago Sanchez ; Thomas Schierl ; Cornelius Hellge
CATEGORY: cs.CV [cs.CV, eess.IV]
HIGHLIGHT: In this work, we present a compression model that aims to minimize the entropy in the frequency domain in order to effectively reduce the data size.

81, TITLE: Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review
AUTHORS: Ming Li ; Ariunaa Enkhtur ; Beverley Anne Yamamoto ; Fei Cheng
CATEGORY: cs.CY [cs.CY, cs.AI]
HIGHLIGHT: Given the increasing usage of ChatGPT and other GAI by students, faculty members, and staff in higher education institutions (HEIs), there is an urgent need to examine the ethical issues involved such as its potential biases. In this scoping review, we clarify the ways in which biases related to GAI in higher education settings have been discussed in recent academic publications and identify what type of potential biases are commonly reported in this body of literature.

82, TITLE: FRAD: Front-Running Attacks Detection on Ethereum Using Ternary Classification Model
AUTHORS: YUHENG ZHANG et. al.
CATEGORY: cs.CR [cs.CR, cs.AI]
HIGHLIGHT: The primary objective of this paper is to delve into a comprehensive classification of transactions associated with front-running attacks, which aims to equip developers with specific strategies to counter each type of attack.

83, TITLE: RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice
AUTHORS: Piotr Sowinski ; Pawel Szmeja ; Maria Ganzha ; Marcin Paprzycki
CATEGORY: cs.DB [cs.DB, cs.AI]
HIGHLIGHT: Over the years, RDF streaming was explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming solutions, due to the lack of a common language.

84, TITLE: Counting Solutions to Conjunctive Queries: Structural and Hybrid Tractability
AUTHORS: Hubie Chen ; Gianluigi Greco ; Stefan Mengel ; Francesco Scarcello
CATEGORY: cs.DB [cs.DB, cs.AI, H.2.4; F.2.2]
HIGHLIGHT: We establish the feasibility of counting answers in polynomial time for classes of queries featuring bounded #-hypertree width.

85, TITLE: TCuPGAN: A Novel Framework Developed for Optimizing Human-machine Interactions in Citizen Science
AUTHORS: RAMANAKUMAR SANKAR et. al.
CATEGORY: cs.HC [cs.HC, cs.CV, cs.LG]
HIGHLIGHT: In the era of big data in scientific research, there is a necessity to leverage techniques which reduce human effort in labeling and categorizing large datasets by involving sophisticated machine tools. To combat this problem, we present a novel, general purpose model for 3D segmentation that leverages patch-wise adversariality and Long Short-Term Memory to encode sequential information.

86, TITLE: Pseudo-label Correction for Instance-dependent Noise Using Teacher-student Framework
AUTHORS: Eugene Kim
CATEGORY: cs.LG [cs.LG, cs.CV]
HIGHLIGHT: The inability to differentiate clean and noisy labels ultimately results in poor generalization. We approach this problem by reassigning the label for each image using a new teacher-student based framework termed P-LC (pseudo-label correction).

87, TITLE: LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design
AUTHORS: Niklas Dobberstein ; Astrid Maass ; Jan Hamaekers
CATEGORY: cs.LG [cs.LG, cs.AI, physics.chem-ph]
HIGHLIGHT: Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present "LLamol", a single novel generative transformer model based on the LLama 2 architecture, which was trained on a 13M superset of organic compounds drawn from diverse public sources.

88, TITLE: Comparative Analysis of Transformers for Modeling Tabular Data: A Casestudy Using Industry Scale Dataset
AUTHORS: USNEEK SINGH et. al.
CATEGORY: cs.LG [cs.LG, cs.AI]
HIGHLIGHT: The paper presents crucial insights into optimal data pre-processing, compares pre-training and direct supervised learning methods, discusses strategies for managing categorical and numerical features, and highlights trade-offs between computational resources and performance.

89, TITLE: Evolutionary Game Theory: The Mathematics of Evolution and Collective Behaviours
AUTHORS: The Anh Han
CATEGORY: cs.MA [cs.MA, cs.AI, math-ph, math.DS, math.MP, nlin.AO]
HIGHLIGHT: This brief discusses evolutionary game theory as a powerful and unified mathematical tool to study evolution of collective behaviours.

90, TITLE: Federated Transformed Learning for A Circular, Secure, and Tiny AI
AUTHORS: Weisi Guo ; Schyler Sun ; Bin Li ; Sam Blakeman
CATEGORY: cs.NI [cs.NI, cs.AI, cs.LG]
HIGHLIGHT: Here we set out the vision to achieve transformed DL representations across a 5G and Beyond networked architecture.

91, TITLE: Digital Twin-Native AI-Driven Service Architecture for Industrial Networks
AUTHORS: Kubra Duran ; Matthew Broadbent ; Gokhan Yurdakul ; Berk Canberk
CATEGORY: cs.NI [cs.NI, cs.AI]
HIGHLIGHT: In addition to these, the current intelligent mechanisms cannot perform well due to the spatiotemporal changes in the implemented IoT network scenario. To handle these challenges, we propose a DT-native AI-driven service architecture in support of the concept of IoT networks.

92, TITLE: A Survey and Analysis of Evolutionary Operators for Permutations
AUTHORS: Vincent A. Cicirello
CATEGORY: cs.NE [cs.NE, cs.AI, cs.DM, 68W50 (Primary), 05A05, 68T05, 68T20, 68W20, 90C27, 90C59, I.2.8; F.2.2; G.2.1]
HIGHLIGHT: In this paper, we survey the breadth of evolutionary operators for permutations.

93, TITLE: Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
AUTHORS: JENS E. PEDERSEN et. al.
CATEGORY: cs.NE [cs.NE]
HIGHLIGHT: Here, we establish a common reference-frame for computations in neuromorphic systems, dubbed the Neuromorphic Intermediate Representation (NIR).

94, TITLE: Anyone Can Code: Algorithmic Thinking
AUTHORS: Ali Arya
CATEGORY: cs.PL [cs.PL]
HIGHLIGHT: Anyone Can Code: Algorithmic Thinking

95, TITLE: Typed Compositional Quantum Computation with Lenses
AUTHORS: Jacques Garrigue ; Takafumi Saikawa
CATEGORY: cs.PL [cs.PL, cs.LO]
HIGHLIGHT: We propose a type-theoretic framework for describing and proving properties of quantum computations, in particular those presented as quantum circuits.

96, TITLE: Petit Programming Language and Compiler
AUTHORS: Raul Barbosa
CATEGORY: cs.PL [cs.PL]
HIGHLIGHT: The initial tutorials in this series cover the practical applications of the lex and yacc tools, while the concluding tutorial focuses on generating LLVM intermediate representation.

97, TITLE: Racing With ROS 2 A Navigation System for An Autonomous Formula Student Race Car
AUTHORS: Alastair Bradford ; Grant van Breda ; Tobias Fischer
CATEGORY: cs.RO [cs.RO, cs.CV]
HIGHLIGHT: These offer new challenges to aspiring engineers, including the team at QUT Motorsport, but also raise the entry barrier due to the complexity of high-speed navigation and control. This paper presents an open-source solution using the Robot Operating System 2, specifically its open-source navigation stack, to address these challenges in autonomous Formula Student race cars.

98, TITLE: Data-Driven Risk Modeling for Infrastructure Projects Using Artificial Intelligence Techniques
AUTHORS: Abdolmajid Erfani
CATEGORY: cs.SE [cs.SE, cs.AI]
HIGHLIGHT: How well does the project team identify and evaluate risks in the initial phase compared to what happens during project execution? Using historical data and artificial intelligence techniques, this study addressed these limitations by introducing a data-driven framework to identify risks automatically and to examine the quality of early risk registers and risk assessments.

99, TITLE: TinyCLAP: Distilling Constrastive Language-Audio Pretrained Models
AUTHORS: Francesco Paissan ; Elisabetta Farella
CATEGORY: cs.SD [cs.SD, cs.CL, cs.LG, eess.AS]
HIGHLIGHT: This paper investigates how we can reduce the complexity of contrastive language-audio pre-trained models, yielding an efficient model that we call tinyCLAP.

100, TITLE: Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
AUTHORS: CHRISTOPH KERN et. al.
CATEGORY: stat.ML [stat.ML, cs.CL, cs.LG, stat.ME]
HIGHLIGHT: We introduce the term annotation sensitivity to refer to the impact of annotation data collection methods on the annotations themselves and on downstream model performance and predictions.

101, TITLE: SER_AMPEL: A Multi-source Dataset for SER of Italian Older Adults
AUTHORS: Alessandra Grossi ; Francesca Gasparini
CATEGORY: eess.AS [eess.AS, cs.CL, cs.SD]
HIGHLIGHT: In this paper, SER_AMPEL, a multi-source dataset for speech emotion recognition (SER) is presented.

102, TITLE: Latent Diffusion Prior Enhanced Deep Unfolding for Spectral Image Reconstruction
AUTHORS: Zongliang Wu ; Ruiying Lu ; Ying Fu ; Xin Yuan
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this paper, we introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to enhance the regression-based deep unfolding method.

103, TITLE: Enhancing MTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT
AUTHORS: Hanem Ellethy ; Shekhar S. Chandra ; Viktor Vegh
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: In this study, we introduce an innovative approach to enhance mTBI diagnosis using 3D Computed Tomography (CT) images and a metric learning technique trained with triplet loss.

104, TITLE: Unsupervised High-throughput Segmentation of Cells and Cell Nuclei in Quantitative Phase Images
AUTHORS: Julia Sistermanns ; Ellen Emken ; Gregor Weirich ; Oliver Hayden ; Wolfgang Utschick
CATEGORY: eess.IV [eess.IV, cs.CV, q-bio.CB, q-bio.QM]
HIGHLIGHT: In the effort to aid cytologic diagnostics by establishing automatic single cell screening using high throughput digital holographic microscopy for clinical studies thousands of images and millions of cells are captured.

105, TITLE: Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy
AUTHORS: LAURA ALVAREZ-FLOREZ et. al.
CATEGORY: eess.IV [eess.IV, cs.AI]
HIGHLIGHT: Thereafter, to quantify cardiac motion, we calculate global and segmental strain over a cardiac cycle and compute the peak strain.

106, TITLE: Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
AUTHORS: MATTHIAS HADLICH et. al.
CATEGORY: eess.IV [eess.IV, cs.AI, cs.CV, cs.HC]
HIGHLIGHT: This requirement presents a challenge for whole-body Positron Emission Tomography (PET) imaging, where lesions are scattered throughout the body. To tackle this problem, we introduce SW-FastEdit - an interactive segmentation framework that accelerates the labeling by utilizing only a few user clicks instead of voxelwise annotations.

107, TITLE: Deformable Multi-modal Image Registration for The Correlation Between Optical Measurements and Histology Images
AUTHORS: Lianne Feenstra ; Maud Lambregts ; Theo J. M Ruers ; Behdad Dashtbozorg
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: This study explores an automated multi-modal image registration technique utilizing deep learning principles to align snapshot breast specimen images with corresponding histology images.

108, TITLE: Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images Using Adversarial Training
AUTHORS: Karina Yang ; Alexis Bennett ; Dominique Duncan
CATEGORY: eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT: Artificial intelligence (AI) algorithms applied to chest X-ray (CXR) images have emerged as promising diagnostic tools, and previous work has demonstrated impressive classification performances.

109, TITLE: CT-xCOV: A CT-scan Based Explainable Framework for COVid-19 Diagnosis
AUTHORS: Ismail Elbouknify ; Afaf Bouhoute ; Khalid Fardousse ; Ismail Berrada ; Abdelmajid Badri
CATEGORY: eess.IV [eess.IV, cs.CV]
HIGHLIGHT: In this work, CT-xCOV, an explainable framework for COVID-19 diagnosis using Deep Learning (DL) on CT-scans is developed.

110, TITLE: Windformer:Bi-Directional Long-Distance Spatio-Temporal Network For Wind Speed Prediction
AUTHORS: XUEWEI LI et. al.
CATEGORY: eess.SP [eess.SP, cs.AI]
HIGHLIGHT: History and future time information includes the trend of airflow changes, whether this dynamic information can be utilized will also affect the prediction effect. In response to the above problems, this paper proposes Windformer.