Temporal Slowness in Central Vision Drives Semantic Object Learning
Department of Computer Science, Goethe University Frankfurt（法兰克福歌德大学计算机科学系）；The Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；Frankfurt Institute for Advanced Studies（法兰克福高级研究 institute）；Xidian-FIAS international Joint Research Center（西电-法兰克福国际联合研究中心）
Timothy Schaumlöffel, Arthur Aubret, Gemma Roig, Jochen Triesch
Humans acquire semantic object representations from egocentric visual streams with minimal supervision, but the underlying mechanisms remain unclear. Importantly, the visual system only processes the center of its field of view with high resolution and it learns similar representations for visual inputs occurring close in time. This emphasizes slowly changing information around gaze locations. This study investigates the role of central vision and slowness learning in the formation of semantic object representations from human-like visual experience. We simulate five months of human-like visual experience using the Ego4D dataset and a state-of-the-art gaze prediction model. We extract image crops around predicted gaze locations to train a time-contrastive Self-Supervised Learning model. Our results show that exploiting temporal slowness when learning from central visual field experience improves the encoding of different facets of object semantics. Specifically, focusing on central vision strengthens the extraction of foreground object features, while considering temporal slowness, especially in conjunction with eye movements, allows the model to encode broader semantic information about objects. These findings provide new insights into the mechanisms by which humans may develop semantic object representations from natural visual experience. Our code will be made public upon acceptance. Code is available at https://github.com/t9s9/central-vision-ssl.
The Potential and Limitations of Vision-Language Models for Human Motion Understanding: A Case Study in Data-Driven Stroke Rehabilitation
Center for Data Science（数据科学中心）；Tandon School of Engineering（工程学院）；VitalConnect（VitalConnect公司）；Department of Neurology（神经病学部）；Department of Rehabilitation Medicine（康复医学部）；Courant Institute of Mathematical Sciences（数学科学研究所）
Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）
VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
College of Automotive Engineering, Jilin University（吉林大学汽车工程学院）；The National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University（吉林大学汽车底盘集成与生物力学国家级重点实验室）；ReeFocus AI Technology（ReeFocus人工智能技术）
CARE Drive A Framework for Evaluating Reason-Responsiveness of Vision Language Models in Automated Driving
organization= Department of Transport \& Planning, Faculty of Civil Engineering；Geosciences, Delft University of Technology；organization= Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology；organization= Centre for Meaningful Human Control, Delft University of Technology；organization= Faculty of Mechanical Engineering, Delft University of Technology , city= Delft , country= The Netherlands
CARE Drive提出了一种评估视觉语言模型在自动驾驶中推理响应性的框架，通过受控上下文变化比较基线和增强模型决策，验证人类原因对决策的影响。
Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation
School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore（新加坡南洋理工大学交叉学科研究生项目）；University College London（伦敦大学学院）
CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（广东省新型安全智能技术重点实验室，网络空间安全研究院，计算机科学与技术学院，哈尔滨工业大学（深圳））
DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
Department of Civil and Environmental Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校土木与环境工程系）；Lyles School of Civil and Construction Engineering, Purdue University（普渡大学莱尔斯土木与建设工程学院）；Department of Civil Engineering, McGill University（麦吉尔大学土木工程系）
