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ARXIV:2603.08898 · VISUAL QUERY SEGMENTATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08898VISUAL QUERY SEGMENTATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos.
Opportunity summary
Pain A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos. Compared to existing VQL locating only the last appearance of a target using bounding boxes, VQS enables more…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Visual Query Segmentation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos.
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Paper Pack
10.48550/arXiv.2603.08898A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos.
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.
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What was readable
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Dimensions overall score 7.0
PROBLEM
A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos. 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 p...
METHOD
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...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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) localizatio...
WHY NOW
Visual Query Segmentation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Visual Query Segmentation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel visual query segmentation method that enables precise pixel-level localization of objects in untrimmed videos.
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Visual Query Segmentation
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