Event cameras, known for low-latency operation and superior performance in challenging lighting conditions, are suitable for sensitive computer vision tasks such as semantic segmentation in autonomous driving. However, challenges arise due to limited event-based data and the absence of large-scale segmentation benchmarks. Current works are confined to closed-set semantic segmentation, limiting their adaptability to other applications. In this paper, we introduce OVOSE, the first Open-Vocabulary Semantic Segmentation algorithm for Event cameras. OVOSE leverages synthetic event data and knowledge distillation from a pre-trained image-based foundation model to an event-based counterpart, effectively preserving spatial context and transferring open-vocabulary semantic segmentation capabilities. We evaluate the performance of OVOSE on two driving semantic segmentation datasets DDD17, and DSEC-Semantic, comparing it with existing conventional image open-vocabulary models adapted for event-based data. Similarly, we compare OVOSE with state-of-the-art methods designed for closed-set settings in unsupervised domain adaptation for event-based semantic segmentation. OVOSE demonstrates superior performance, showcasing its potential for real-world applications. The code is available at https://github.com/ram95d/OVOSE.
Finger in Camera Speaks Everything: Unconstrained Air-Writing for Real-World
University of Chinese Academy of Sciences（中国科学院大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Shaken, Not Stirred: A Novel Dataset for Visual Understanding of Glasses in Human-Robot Bartending Tasks
Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University（应用信息学院，数学物理学院，科门ius大学）；Knowledge Technology Group, Department of Informatics, University of Hamburg（知识技术组，信息学院，汉堡大学）
3D Feature Distillation with Object-Centric Priors
Department of Artificial Intelligence University of Groningen, the Neteherlands（格罗宁根大学人工智能系）；School of Informatics University of Edinburgh, United Kingdom（爱丁堡大学信息学院）；Department of Computer Science University College London, United Kingdom（伦敦大学学院计算机科学系）
Bridging 3D Gaussians and Semantic Occupancy for Comprehensive Open-Vocabulary Scene Understanding from Unposed Images
The Hong Kong Polytechnic University（香港理工大学）；Eastern Institute of Technology（东方理工大学）；Shanghai Jiao Tong University（上海交通大学）；Wuhan University（武汉大学）；Great Wall Motor（长城汽车）；Tsinghua University（清华大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）
From Perception to Assistance: Open-Vocabulary Shared Autonomy for Robotic Manipulation
University of São Paulo（圣保罗大学）；Instituto Israelita de Ensino e Pesquisa Albert Einstein（以色列爱因斯坦教育与研究机构）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；University of Groningen（格罗宁根大学）；Imagga Technologies Ltd（Imagga技术有限公司）；Digital Spaces Living Lab（数字空间生活实验室）
Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Dalian University of Technology（大连理工大学）
