SOTAVerified

Video Semantic Segmentation

The goal of video semantic segmentation is to assign a predefined class to each pixel in all frames of a video. This requires the model not only to predict accurate segmentation masks but also to ensure that these masks remain temporally consistent across frames. This task has broad applications in areas such as autonomous driving, medical video analysis, and AR/VR.

Papers

Showing 501550 of 895 papers

TitleStatusHype
Reliable Propagation-Correction Modulation for Video Object SegmentationCode1
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation0
End-to-End Referring Video Object Segmentation with Multimodal TransformersCode1
Learning To Segment Dominant Object Motion From Watching Videos0
Hierarchical interaction network for video object segmentation from referring expressions0
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation0
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical FlowCode1
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
Dense Unsupervised Learning for Video SegmentationCode1
Video Salient Object Detection via Contrastive Features and Attention Modules0
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic PerspectiveCode1
SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation0
Perceptual Consistency in Video Segmentation0
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic SegmentationCode1
Multi-Object Tracking and Segmentation with a Space-Time Memory Network0
Temporally stable video segmentation without video annotations0
ViSeRet: A simple yet effective approach to moment retrieval via fine-grained video segmentation0
Pixel-Level Bijective Matching for Video Object SegmentationCode1
Hierarchical Memory Matching Network for Video Object SegmentationCode1
Space Time Recurrent Memory Network0
Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models0
Shifted Chunk Transformer for Spatio-Temporal Representational Learning0
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane DetectionCode1
Joint Inductive and Transductive Learning for Video Object SegmentationCode1
Full-Duplex Strategy for Video Object SegmentationCode1
Self-Supervised Video Object Segmentation by Motion-Aware Mask PropagationCode1
Accelerating Video Object Segmentation with Compressed VideoCode1
Domain Adaptive Video Segmentation via Temporal Consistency RegularizationCode1
Unsupervised Domain Adaptation for Video Semantic Segmentation0
MeNToS: Tracklets Association with a Space-Time Memory Network0
Fast Pixel-Matching for Video Object SegmentationCode0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations0
A Survey on Deep Learning Technique for Video SegmentationCode1
Reciprocal Transformations for Unsupervised Video Object SegmentationCode1
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
Video Object Segmentation Using Global and Instance Embedding Learning0
MSN: Efficient Online Mask Selection Network for Video Instance SegmentationCode0
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object SegmentationCode1
Associating Objects with Transformers for Video Object SegmentationCode1
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation0
TransVOS: Video Object Segmentation with TransformersCode1
Polygonal Point Set TrackingCode1
Attention-guided Temporally Coherent Video Object MattingCode1
Coarse to Fine Multi-Resolution Temporal Convolutional NetworkCode1
DAVOS: Semi-Supervised Video Object Segmentation via Adversarial Domain Adaptation0
Cross-Modal Progressive Comprehension for Referring SegmentationCode1
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence LearningCode1
Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos0
Guided Interactive Video Object Segmentation Using Reliability-Based Attention MapsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TMANet-50mIoU80.3Unverified
2DeltaDist-DDRNet-39mIoU79.9Unverified
3TDNet-50 [9]mIoU79.9Unverified
4PSPNet-101 [20]mIoU79.7Unverified
5PSPNet-50 [20]mIoU78.1Unverified
6LVS [12]mIoU76.8Unverified
7GRFP [15]mIoU73.6Unverified
8FCN-50 [14]mIoU70.1Unverified
9DFF [22]mIoU69.2Unverified
#ModelMetricClaimedVerifiedStatus
1TMANet-50Mean IoU76.5Unverified
2ETC-MobileNetMean IoU76.3Unverified
3TDNet-50Mean IoU76.2Unverified
4PSPNet-50Mean IoU76Unverified
5NetwarpMean IoU74.7Unverified
6GRFPMean IoU67.1Unverified
#ModelMetricClaimedVerifiedStatus
1DVIS++(VIT-L)mIoU63.8Unverified
2UniVS(Swin-L)mIoU59.8Unverified
3Tube-Link(Swin-large)mIoU59.6Unverified
4MRCFA(MiT-B5)mIoU49.9Unverified
5CFFM(MiT-B5)mIoU49.3Unverified
#ModelMetricClaimedVerifiedStatus
1WaSR-T (ResNet-101)Q60.1Unverified
2TMANet (ResNet-50)Q57.5Unverified
3CSANet (ResNet-101)Q49.1Unverified
#ModelMetricClaimedVerifiedStatus
1MVNet(DeepLabV3)mIoU54.52Unverified
2MVNet(PSPNet)mIoU54.36Unverified
3MVNet(FCN)mIoU53.9Unverified