Video Object Segmentation
Overview
Video object segmentation studies how to identify an object in a video and maintain an accurate pixel-level mask as it moves, changes appearance, disappears, or reappears. Common approaches propagate masks using temporal attention, object queries, optical flow, or memory banks; newer systems adapt promptable foundation models such as SAM2 and SAM3, sometimes guided by language, audio, or 3D geometry. Researchers evaluate these methods on benchmarks such as DAVIS, YouTube-VOS, MOSE, and long-video or referring-segmentation datasets, with growing attention to real-time, medical, low-light, and other specialized settings. A central challenge is balancing long-term identity and temporal stability against memory use and inference speed, especially when targets are absent or ambiguous and benchmarks may not fully capture those failures.
Latest papers
114 papers16 papers in the last four weeks, up 78% on the four weeks before. 0.1% of all new papers.