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 126–147 of 147 papers

TitleStatusHype
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation—0
Flow-guided Semi-supervised Video Object Segmentation—0
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching—0
Fast video object segmentation with Spatio-Temporal GANs—0
TrickVOS: A Bag of Tricks for Video Object Segmentation—0
Fast Video Object Segmentation via Dynamic Targeting Network—0
DAVOS: Semi-Supervised Video Object Segmentation via Adversarial Domain Adaptation—0
VideoMatch: Matching based Video Object Segmentation—0
Pixel-Level Equalized Matching for Video Object Segmentation—0
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks—0
PMVOS: Pixel-Level Matching-Based Video Object Segmentation—0
Collaborative Attention Memory Network for Video Object Segmentation—0
CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF—0
Online Video Object Segmentation via Convolutional Trident Network—0
Video Object Segmentation using Tracked Object Proposals—0
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning—0
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation—0
ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation—0
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation—0
Region Aware Video Object Segmentation with Deep Motion Modeling—0
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation—0
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation—0
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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