Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.08898 (cs)
[Submitted on 9 Mar 2026]
Title:Towards Visual Query Segmentation in the Wild
Authors:Bing Fan, Minghao Li, Hanzhi Zhang, Shaohua Dong, Naga Prudhvi Mareedu, Weishi Shi, Yunhe Feng, Yan Huang, Heng Fan
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Abstract:In this paper, we introduce visual query segmentation (VQS), a new paradigm of visual query localization (VQL) that aims to segment all pixel-level occurrences of an object of interest in an untrimmed video, given an external visual query. Compared to existing VQL locating only the last appearance of a target using bounding boxes, VQS enables more comprehensive (i.e., all object occurrences) and precise (i.e., pixel-level masks) localization, making it more practical for real-world scenarios. To foster research on this task, we present VQS-4K, a large-scale benchmark dedicated to VQS. Specifically, VQS-4K contains 4,111 videos with more than 1.3 million frames and covers a diverse set of 222 object categories. Each video is paired with a visual query defined by a frame outside the search video and its target mask, and annotated with spatial-temporal masklets corresponding to the queried target. To ensure high quality, all videos in VQS-4K are manually labeled with meticulous inspection and iterative refinement. To the best of our knowledge, VQS-4K is the first benchmark specifically designed for VQS. Furthermore, to stimulate future research, we present a simple yet effective method, named VQ-SAM, which extends SAM 2 by leveraging target-specific and background distractor cues from the video to progressively evolve the memory through a novel multi-stage framework with an adaptive memory generation (AMG) module for VQS, significantly improving the performance. In our extensive experiments on VQS-4K, VQ-SAM achieves promising results and surpasses all existing approaches, demonstrating its effectiveness. With the proposed VQS-4K and VQ-SAM, we expect to go beyond the current VQL paradigm and inspire more future research and practical applications on VQS. Our benchmark, code, and results will be made publicly available.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.08898 [cs.CV] |
| (or arXiv:2603.08898v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08898
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View a PDF of the paper titled Towards Visual Query Segmentation in the Wild, by Bing Fan and 8 other authors
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