The recognition and understanding of traffic incidents, particularly traffic accidents, is a topic of paramount importance in the realm of intelligent transportation systems and intelligent vehicles. This area has continually captured the extensive focus of both the academic and industrial sectors. Identifying and comprehending complex traffic events is highly challenging, primarily due to the intricate nature of traffic environments, diverse observational perspectives, and the multifaceted causes of accidents. These factors have persistently impeded the development of effective solutions. The advent of large vision-language models (VLMs) such as GPT-4V, has introduced innovative approaches to addressing this issue. In this paper, we explore the ability of GPT-4V with a set of representative traffic incident videos and delve into the model's capacity of understanding these complex traffic situations. We observe that GPT-4V demonstrates remarkable cognitive, reasoning, and decision-making ability in certain classic traffic events. Concurrently, we also identify certain limitations of GPT-4V, which constrain its understanding in more intricate scenarios. These limitations merit further exploration and resolution.
Resolving Ambiguity in Gaze-Facilitated Visual Assistant Interaction Paradigm
Key Laboratory of Pervasive Computing, Tsinghua University（清华大学普适计算重点实验室）；The University of New South Wales（新南威尔士大学）；SKLSDE Lab, Beihang University（北航SKLSDE实验室）
Vrije University of Amsterdam University of Amsterdam Centrum Wiskunde \& Informatic；University of Amsterdam National Institute of Informatics Centrum Wiskunde \& Informatic；Centrum Wiskunde \& Informatic Leiden University University College London；Vrije University of Amsterdam；Centrum Wiskunde \& Informatica Technische Universiteit Delft Guangzhou University；Vrije University of Amsterdam Centrum Wiskunde \& Informatic
IC-EO: Interpretable Code-based assistant for Earth Observation
Université Paris Cité, LIPADE, F-75006 Paris, France askEarth AG, Zurich, Switzerland ESA -lab, Frascati, Italy ESA, Advanced Concepts；Studies Office, Noordwijk, the Netherlands
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛-麻省理工博德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Harvard John A. Paulson School of Engineering And Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Harvard University（哈佛大学）；Wexner Medical Center, Ohio State University（俄亥俄州立大学韦克斯纳医学中心）；Ohio State University（俄亥俄州立大学）；Harvard Data Science Initiative（哈佛大学数据科学计划）
VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding
The Chinese University of Hong Kong（香港中文大学）；Ant Group（蚂蚁集团）；Tsinghua University（清华大学）；Tongji University（同济大学）；The Hong Kong University of Science and Technology（香港科技大学）
