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 26–50 of 147 papers

TitleStatusHype
Robust and Efficient Memory Network for Video Object Segmentation—0
CLVOS23: A Long Video Object Segmentation Dataset for Continual LearningCode0
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation—0
Flow-guided Semi-supervised Video Object Segmentation—0
Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation—0
Look Before You Match: Instance Understanding Matters in Video Object Segmentation—0
Learning to Learn Better for Video Object SegmentationCode1
Decoupling Features in Hierarchical Propagation for Video Object SegmentationCode2
Global Spectral Filter Memory Network for Video Object SegmentationCode1
Pixel-Level Equalized Matching for Video Object Segmentation—0
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationCode1
Per-Clip Video Object SegmentationCode1
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation—0
Region Aware Video Object Segmentation with Deep Motion Modeling—0
Learning Quality-aware Dynamic Memory for Video Object SegmentationCode1
Tackling Background Distraction in Video Object SegmentationCode1
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory ModelCode3
Towards Robust Video Object Segmentation with Adaptive Object CalibrationCode1
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation—0
Collaborative Attention Memory Network for Video Object Segmentation—0
Recurrent Dynamic Embedding for Video Object SegmentationCode1
Boosting Video Object Segmentation based on Scale InconsistencyCode0
Adaptive Memory Management for Video Object SegmentationCode0
Scalable Video Object Segmentation with Identification MechanismCode2
MixFormer: End-to-End Tracking with Iterative Mixed AttentionCode2
Show:102550
← PrevPage 2 of 6Next →

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