Papers › Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation

Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation

4 Sep 2022arXiv:2209.03138archive 2025-07-28

Suhwan Cho, Minhyeok Lee, Seunghoon Lee, Chaewon Park, Donghyeong Kim, Sangyoun Lee

Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods leverage motion cues obtained from optical flow maps in addition to appearance cues to exploit the property that salient objects usually have distinctive movements compared to the background. However, as they are overly dependent on motion cues, which may be unreliable in some cases, they cannot achieve stable prediction. To reduce this motion dependency of existing two-stream VOS methods, we propose a novel motion-as-option network that optionally utilizes motion cues. Additionally, to fully exploit the property of the proposed network that motion is not always required, we introduce a collaborative network learning strategy. On all the public benchmark datasets, our proposed network affords state-of-the-art performance with real-time inference speed.

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Tasks

Optical Flow EstimationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Video Object Segmentation DAVIS 2016 val TMO (MiT-b1) F 87.8 #6 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val TMO (MiT-b1) G 87.2 #6 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val TMO (MiT-b1) J 86.6 #6 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val TMO (RN-101) F 86.6 #7 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val TMO (RN-101) G 86.1 #7 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation DAVIS 2016 val TMO (RN-101) J 85.6 #7 of 25 Archive leaderboard report
Unsupervised Video Object Segmentation FBMS test TMO (MiT-b1) J 80.0 #6 of 15 Archive leaderboard report
Unsupervised Video Object Segmentation FBMS test TMO (RN-101) J 79.9 #7 of 15 Archive leaderboard report
Unsupervised Video Object Segmentation YouTube-Objects TMO (RN-101) J 71.5 #7 of 16 Archive leaderboard report
Unsupervised Video Object Segmentation YouTube-Objects TMO (MiT-b1) J 71.1 #9 of 16 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

VOS

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