Video summarization is a crucial research area that aims to efficiently browse and retrieve relevant information from the vast amount of video content available today. With the exponential growth of multimedia data, the ability to extract meaningful representations from videos has become essential. Video summarization techniques automatically generate concise summaries by selecting keyframes, shots, or segments that capture the video's essence. This process improves the efficiency and accuracy of various applications, including video surveillance, education, entertainment, and social media. Despite the importance of video summarization, there is a lack of diverse and representative datasets, hindering comprehensive evaluation and benchmarking of algorithms. Existing evaluation metrics also fail to fully capture the complexities of video summarization, limiting accurate algorithm assessment and hindering the field's progress. To overcome data scarcity challenges and improve evaluation, we propose an unsupervised approach that leverages video data structure and information for generating informative summaries. By moving away from fixed annotations, our framework can produce representative summaries effectively. Moreover, we introduce an innovative evaluation pipeline tailored specifically for video summarization. Human participants are involved in the evaluation, comparing our generated summaries to ground truth summaries and assessing their informativeness. This human-centric approach provides valuable insights into the effectiveness of our proposed techniques. Experimental results demonstrate that our training-free framework outperforms existing unsupervised approaches and achieves competitive results compared to state-of-the-art supervised methods.
OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness
Autonomous Intelligent Systems group, Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany（自主智能系统组，计算机科学研究所VI——智能系统与机器人——以及机器人中心和拉玛尔人工智能与机器学习研究所，波恩大学，德国）
Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电子与计算机工程系）；Amazon（亚马逊）；Samsung Research（三星研究院）；School of Computer Science and Engineering, Soongsil University（顺天大学计算机科学与工程学院）；IPAI, AIIS, ASRI, INMC, and ISRC, Seoul National University（首尔国立大学IPAI、AIIS、ASRI、INMC和ISRC）
ELMAR: Enhancing LiDAR Detection with 4D Radar Motion Awareness and Cross-modal Uncertainty
Infineon Technologies AG and the Technical University of Munich（英飞凌科技有限公司和技术大学慕尼黑）；China University of Geosciences（中国地质大学）；Technical University of Munich（技术大学慕尼黑）
Enhancing Maritime Situational Awareness through End-to-End Onboard Raw Data Analysis
European Space Agency（欧洲空间局）；University of Naples Federico II（那不勒斯费德里科二世大学）；Ben-Gurion University of the Negev（本-古里安大学内盖夫分校）；University of Campania “L. Vanvitelli”（坎帕尼亚大学“L.万维泰利”分校）；Advanced Concepts and Studies Office, European Space Agency（欧洲空间局先进概念与研究办公室）
