SOTAVerified

Semi-Supervised Video Object Segmentation

The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.

Papers

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Showing 1–10 of 147 papers

TitleStatusHype
THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation—0
Exploring Enhanced Contextual Information for Video-Level Object TrackingCode2
A Distractor-Aware Memory for Visual Object Tracking with SAM2Code3
LiVOS: Light Video Object Segmentation with Gated Linear MatchingCode1
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation—0
SAM 2: Segment Anything in Images and VideosCode11
Global Motion Understanding in Large-Scale Video Object Segmentation—0
Spatial-Temporal Multi-level Association for Video Object Segmentation—0
Efficient Video Object Segmentation via Modulated Cross-Attention MemoryCode2
Video Object Segmentation with Dynamic Query ModulationCode1
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All datasetsDAVIS 2017 (val)DAVIS 2016DAVIS-2017 (test-dev)YouTube-VOS 2018DAVIS (no YouTube-VOS training)YouTube-VOS 2019VOT2020MOSELong Video DatasetYouTubeDAVIS 2017BURST-test

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Cutie (base, MEGA, 600 pixels)HOTA (all)66—Unverified
2Cutie (base, with mose, 600 pixels)HOTA (all)62.6—Unverified