MOVE: Motion-Guided Few-Shot Video Object Segmentation

• URL: http://arxiv.org/abs/2507.22061v1
• Date: Tue, 29 Jul 2025 17:59:35 GMT
• Title: MOVE: Motion-Guided Few-Shot Video Object Segmentation
• Authors: Kaining Ying, Hengrui Hu, Henghui Ding,
• Abstract summary: This work addresses motion-guided few-shot video object segmentation (FSVOS)<n>It aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns.<n>We introduce MOVE, a large-scale dataset specifically designed for motion-guided FSVOS.
• Score: 25.624419551994354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Segment Any Motion in Videos [80.72424676419755]
  We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features.<n>Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support.
  arXiv  Detail & Related papers  (2025-03-28T09:34:11Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions [93.35942025232943]
  We propose a large-scale dataset called MeViS, which contains numerous motion expressions to indicate target objects in complex environments. The goal of our benchmark is to provide a platform that enables the development of effective language-guided video segmentation algorithms.
  arXiv  Detail & Related papers  (2023-08-16T17:58:34Z)
• Event-Free Moving Object Segmentation from Moving Ego Vehicle [88.33470650615162]
  Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving. Most segmentation methods leverage motion cues obtained from optical flow maps. We propose to exploit event cameras for better video understanding, which provide rich motion cues without relying on optical flow.
  arXiv  Detail & Related papers  (2023-04-28T23:43:10Z)
• InstMove: Instance Motion for Object-centric Video Segmentation [70.16915119724757]
  In this work, we study the instance-level motion and present InstMove, which stands for Instance Motion for Object-centric Video. In comparison to pixel-wise motion, InstMove mainly relies on instance-level motion information that is free from image feature embeddings. With only a few lines of code, InstMove can be integrated into current SOTA methods for three different video segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-14T17:58:44Z)
• Learning to Segment Rigid Motions from Two Frames [72.14906744113125]
  We propose a modular network, motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel.
  arXiv  Detail & Related papers  (2021-01-11T04:20:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.