AgentRVOS for MeViS-Text Track of 5th PVUW Challenge: 3rd Method
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915955878133760 |
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| author | Miao, Deshui Yang, Chao Tian, Chao Zhu, Guoqing Yang, Kai Mo, Zhifan Li, Xin |
| author_facet | Miao, Deshui Yang, Chao Tian, Chao Zhu, Guoqing Yang, Kai Mo, Zhifan Li, Xin |
| contents | This report describes a Ref-VOS pipeline centered on Sa2VA and organized with explicit agent roles. The key idea is that Sa2VA should provide the first dense semantic hypothesis, while an agent loop decides whether that hypothesis should be accepted, revised, or refined. The pipeline starts with a target-presence judgment stage. If the referred object does not exist in the video, the system directly outputs zero masks. Otherwise, Sa2VA receives the video and referring prompt and produces a coarse mask trajectory over the full video. This trajectory is treated as a semantic prior rather than a final answer. A planner agent decomposes the query, temporal partition agents identify informative blocks, scout agents search for anchor frames, and refinement agents convert reliable Sa2VA masks into boxes and points for SAM3 propagation. A critic scores candidate trajectories, a reflection controller repairs weak hypotheses, and a collaboration controller reconciles multiple agent branches. The result is a Ref-VOS system in which Sa2VA is responsible for dense grounded understanding, while the agent layer handles presence verification, temporal search, confidence-aware revision, and final mask refinement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22836 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgentRVOS for MeViS-Text Track of 5th PVUW Challenge: 3rd Method Miao, Deshui Yang, Chao Tian, Chao Zhu, Guoqing Yang, Kai Mo, Zhifan Li, Xin Computer Vision and Pattern Recognition This report describes a Ref-VOS pipeline centered on Sa2VA and organized with explicit agent roles. The key idea is that Sa2VA should provide the first dense semantic hypothesis, while an agent loop decides whether that hypothesis should be accepted, revised, or refined. The pipeline starts with a target-presence judgment stage. If the referred object does not exist in the video, the system directly outputs zero masks. Otherwise, Sa2VA receives the video and referring prompt and produces a coarse mask trajectory over the full video. This trajectory is treated as a semantic prior rather than a final answer. A planner agent decomposes the query, temporal partition agents identify informative blocks, scout agents search for anchor frames, and refinement agents convert reliable Sa2VA masks into boxes and points for SAM3 propagation. A critic scores candidate trajectories, a reflection controller repairs weak hypotheses, and a collaboration controller reconciles multiple agent branches. The result is a Ref-VOS system in which Sa2VA is responsible for dense grounded understanding, while the agent layer handles presence verification, temporal search, confidence-aware revision, and final mask refinement. |
| title | AgentRVOS for MeViS-Text Track of 5th PVUW Challenge: 3rd Method |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.22836 |