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 5175 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 Segmentation0
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 Segmentation0
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 Adaptation0
Learning Position and Target Consistency for Memory-based Video Object Segmentation0
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&F90.7Unverified
2Cutie+ (base)J&F90.5Unverified
3ISVOS (BL30K, MS)J&F89.8Unverified
4XMem (BL30K, MS)J&F89.5Unverified
5ISVOS (MS)J&F88.6Unverified
6ISVOS (BL30K)J&F88.2Unverified
7XMem (MS)J&F88.2Unverified
8JIMDJ&F88.1Unverified
9Cutie+ (base, MEGA)J&F88.1Unverified
10Cutie (base)J&F87.9Unverified
#ModelMetricClaimedVerifiedStatus
1SwinB-AOTv2-L (MS)J&F93Unverified
2SwinB-AOST (L'=3, MS)J&F93Unverified
3SwinB-DeAOT-LJ&F92.9Unverified
4XMem (MS)J&F92.7Unverified
5SwinB-AOST (L'=3)J&F92.4Unverified
6SwinB-AOTv2-LJ&F92.4Unverified
7R50-DeAOT-LJ&F92.3Unverified
8R50-AOST (L'=3)J&F92.1Unverified
9SwinB-AOT-LJ&F92Unverified
10XMem (BL30K)J&F92Unverified
#ModelMetricClaimedVerifiedStatus
1Cutie+ (base, MEGA)J&F88.1Unverified
2Cutie (base, MEGA)J&F86.1Unverified
3Cutie+ (base)J&F85.9Unverified
4SwinB-AOST (L'=3, MS)J&F84.7Unverified
5SwinB-AOTv2-LJ&F84.5Unverified
6JIMD-R50J&F83.9Unverified
7XMem (BL30K, MS)J&F83.7Unverified
8DEVAJ&F83.2Unverified
9XMem (MS)J&F83.1Unverified
10SwinB-DeAOT-LJ&F82.8Unverified