mlsanityData to 14 Oct 2026

MOVE: Motion-Guided Few-Shot Video Object Segmentation

arXiv 2507.22061 ↗First posted 29 Jul 2025cs.CVcited by 6

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Abstract (arXiv)

This work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns. Existing FSVOS datasets and methods typically focus on object categories, which are static attributes that ignore the rich temporal dynamics in videos, limiting their application in scenarios requiring motion understanding. To fill this gap, we introduce MOVE, a large-scale dataset specifically designed for motion-guided FSVOS. Based on MOVE, we comprehensively evaluate 6 state-of-the-art methods from 3 different related tasks across 2 experimental settings. Our results reveal that current methods struggle to address motion-guided FSVOS, prompting us to analyze the associated challenges and propose a baseline method, Decoupled Motion Appearance Network (DMA). Experiments demonstrate that our approach achieves superior performance in few shot motion understanding, establishing a solid foundation for future research in this direction.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 46 of its 72 references that are in the map. First posted on arXiv 29 Jul 2025.

Builds on

46 of 72 references.

References · 46

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34 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenescited by 62
  2. 2025Multimodal Referring Segmentation: A Surveycited by 22
  3. 2025Towards Omnimodal Expressions and Reasoning in Referring Audio-Visual Segmentationcited by 11
  4. 2026Geometry-Guided Camera Motion Understanding in VideoLLMscited by 3
  5. 2026FeVOS: Foresight Expression Video Object Segmentationcited by 0
  6. 2026VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Trackingcited by 0