Papers › Self-supervised Video Object Segmentation by Motion Grouping

Self-supervised Video Object Segmentation by Motion Grouping

15 Apr 2021ICCV 2021 10arXiv:2104.07658archive 2025-07-28

Charig Yang, Hala Lamdouar, Erika Lu, Andrew Zisserman, Weidi Xie

Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. We make the following contributions: First, we introduce a simple variant of the Transformer to segment optical flow frames into primary objects and the background. Second, we train the architecture in a self-supervised manner, i.e. without using any manual annotations. Third, we analyze several critical components of our method and conduct thorough ablation studies to validate their necessity. Fourth, we evaluate the proposed architecture on public benchmarks (DAVIS2016, SegTrackv2, and FBMS59). Despite using only optical flow as input, our approach achieves superior or comparable results to previous state-of-the-art self-supervised methods, while being an order of magnitude faster. We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.

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Tasks

Motion Segmentation Object Optical Flow Estimation Semantic Segmentation Unsupervised Object Segmentation Video Object Segmentation Video Segmentation Video Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Unsupervised Object Segmentation	DAVIS 2016	MG	J score	68.3	#7 of 9	Archive leaderboard	report

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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