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 51–75 of 147 papers

TitleStatusHype
Siamese Network with Interactive Transformer for Video Object SegmentationCode0
Reliable Propagation-Correction Modulation for Video Object SegmentationCode1
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation—0
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical FlowCode1
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation—0
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
DAVOS: Semi-Supervised Video Object Segmentation via Adversarial Domain Adaptation—0
Learning Position and Target Consistency for Memory-based Video Object Segmentation—0
Efficient Regional Memory Network for Video Object SegmentationCode1
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware FusionCode1
Separable Structure Modeling for Semi-supervised Video Object SegmentationCode0
SwiftNet: Real-time Video Object SegmentationCode1
SSTVOS: Sparse Spatiotemporal Transformers for Video Object SegmentationCode1
Video Object Segmentation With Dynamic Memory Networks and Adaptive Object AlignmentCode0
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object SegmentationCode1
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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