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

Showing 1–50 of 147 papers

TitleStatusHype
SAM 2: Segment Anything in Images and VideosCode12
A Distractor-Aware Memory for Visual Object Tracking with SAM2Code3
Putting the Object Back into Video Object SegmentationCode3
Tracking Anything with Decoupled Video SegmentationCode3
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory ModelCode3
Exploring Enhanced Contextual Information for Video-Level Object TrackingCode2
Efficient Video Object Segmentation via Modulated Cross-Attention MemoryCode2
ODTrack: Online Dense Temporal Token Learning for Visual TrackingCode2
XMem++: Production-level Video Segmentation From Few Annotated FramesCode2
Tracking Anything in High QualityCode2
Video Object Segmentation in Panoptic Wild ScenesCode2
Decoupling Features in Hierarchical Propagation for Video Object SegmentationCode2
Scalable Video Object Segmentation with Identification MechanismCode2
MixFormer: End-to-End Tracking with Iterative Mixed AttentionCode2
Fast Online Object Tracking and Segmentation: A Unifying ApproachCode2
LiVOS: Light Video Object Segmentation with Gated Linear MatchingCode1
Video Object Segmentation with Dynamic Query ModulationCode1
Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment AnythingCode1
Lester: rotoscope animation through video object segmentation and trackingCode1
Learning to Learn Better for Video Object SegmentationCode1
Global Spectral Filter Memory Network for Video Object SegmentationCode1
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationCode1
Per-Clip Video Object SegmentationCode1
Learning Quality-aware Dynamic Memory for Video Object SegmentationCode1
Tackling Background Distraction in Video Object SegmentationCode1
Towards Robust Video Object Segmentation with Adaptive Object CalibrationCode1
Recurrent Dynamic Embedding for Video Object SegmentationCode1
Reliable Propagation-Correction Modulation for Video Object SegmentationCode1
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical FlowCode1
Dense Unsupervised Learning for Video SegmentationCode1
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic PerspectiveCode1
Pixel-Level Bijective Matching for Video Object SegmentationCode1
Hierarchical Memory Matching Network for Video Object SegmentationCode1
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
Self-Supervised Video Object Segmentation by Motion-Aware Mask PropagationCode1
Accelerating Video Object Segmentation with Compressed VideoCode1
Do Different Tracking Tasks Require Different Appearance Models?Code1
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object SegmentationCode1
Associating Objects with Transformers for Video Object SegmentationCode1
TransVOS: Video Object Segmentation with TransformersCode1
Efficient Regional Memory Network for Video Object SegmentationCode1
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware FusionCode1
SwiftNet: Real-time Video Object SegmentationCode1
SSTVOS: Sparse Spatiotemporal Transformers for Video Object SegmentationCode1
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object SegmentationCode1
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box EstimationCode1
Make One-Shot Video Object Segmentation Efficient AgainCode1
TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency LossCode1
Delving into the Cyclic Mechanism in Semi-supervised Video Object SegmentationCode1
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region RefinementCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAM2J&F90.7—Unverified
2Cutie+ (base)J&F90.5—Unverified
3ISVOS (BL30K, MS)J&F89.8—Unverified
4XMem (BL30K, MS)J&F89.5—Unverified
5ISVOS (MS)J&F88.6—Unverified
6ISVOS (BL30K)J&F88.2—Unverified
7XMem (MS)J&F88.2—Unverified
8Cutie+ (base, MEGA)J&F88.1—Unverified
9JIMDJ&F88.1—Unverified
10Cutie (base)J&F87.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinB-AOST (L'=3, MS)J&F93—Unverified
2SwinB-AOTv2-L (MS)J&F93—Unverified
3SwinB-DeAOT-LJ&F92.9—Unverified
4XMem (MS)J&F92.7—Unverified
5SwinB-AOTv2-LJ&F92.4—Unverified
6SwinB-AOST (L'=3)J&F92.4—Unverified
7R50-DeAOT-LJ&F92.3—Unverified
8R50-AOST (L'=3)J&F92.1—Unverified
9R50-AOST (L'=2)J&F92—Unverified
10DeAOT-LJ&F92—Unverified
#ModelMetricClaimedVerifiedStatus
1Cutie+ (base, MEGA)J&F88.1—Unverified
2Cutie (base, MEGA)J&F86.1—Unverified
3Cutie+ (base)J&F85.9—Unverified
4SwinB-AOST (L'=3, MS)J&F84.7—Unverified
5SwinB-AOTv2-LJ&F84.5—Unverified
6JIMD-R50J&F83.9—Unverified
7XMem (BL30K, MS)J&F83.7—Unverified
8DEVAJ&F83.2—Unverified
9XMem (MS)J&F83.1—Unverified
10SwinB-DeAOT-LJ&F82.8—Unverified