Visual reasoning is a long-term goal of vision research. In the last decade, several works have attempted to apply deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of the generalization of the relations learned. In recent years, several innovations in DNNs have been developed in order to enable learning abstract relation from images. In this work, we systematically evaluate a series of DNNs that integrate mechanism such as slot attention, recurrently guided attention, and external memory, in the simplest possible visual reasoning task: deciding whether two objects are the same or different. We found that, although some models performed better than others in generalizing the same-different relation to specific types of images, no model was able to generalize this relation across the board. We conclude that abstract visual reasoning remains largely an unresolved challenge for DNNs.
CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), and University of Chinese Academy of Sciences（智能信息处理重点实验室，计算技术研究所（ICT），中国科学院（CAS）及中国科学院大学）
Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning
National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
Geometric-Guided Few-Shot Dental Landmark Detection with Human-Centric Foundation Model
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China（电子与计算机工程系，香港科学与技术大学）；Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China（牙科学院儿童牙科与正畸学系，香港大学）
Vehicle-centric Perception via Multimodal Structured Pre-training
Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；the School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Department of Electrical & Computer Engineering, National University of Singapore（电气与计算机工程系，新加坡国立大学）；School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）
Beyond Endpoints: Path-Centric Reasoning for Vectorized Off-Road Network Extraction
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥国家综合科学中心人工智能研究所）；Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines, School of Computer Science and Information Engineering, Hefei University of Technology（安徽省情感计算与先进智能机器重点实验室，合肥工业大学计算机科学与信息工程学院）；Department of Computer and Network Engineering, The University of Electro-Communications（电子通信大学计算机与网络工程系）
Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation
Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University（南京大学）；Baidu（百度）
Top-Down Guidance for Learning Object-Centric Representations
MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CAIR, HKSIS, Chinese Academy of Sciences（中国科学院计算机研究所）；School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T（澳门科技大学计算机科学与工程学院）
Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Peng Cheng Laboratory（鹏城实验室）
AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection
Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）；Shenzhen Key Laboratory of Media Security（深圳媒体安全重点实验室）；SZU-AFS Joint Innovation Center for AI Technology（深圳大学-AFS人工智能技术联合创新中心）；Shenzhen University（深圳大学）；School of Cyber Science and Technology（网络安全科学与技术学院）；The Smart City Research Institute of China Electronics Technology Group Corporation（中国电子科技集团有限公司智慧城市研究院）
