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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–895 of 895 papers

TitleStatusHype
Superframes, A Temporal Video Segmentation—0
Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior—0
Super-Trajectory for Video Segmentation—0
Symmetric block-low-rank layers for fully reversible multilevel neural networks—0
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation—0
Temporal Collection and Distribution for Referring Video Object Segmentation—0
Temporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation—0
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation—0
Temporally stable video segmentation without video annotations—0
Temporal Segmentation of Egocentric Videos—0
Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation—0
The 2017 DAVIS Challenge on Video Object Segmentation—0
The 2018 DAVIS Challenge on Video Object Segmentation—0
The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation—0
The 2nd Solution for LSVOS Challenge RVOS Track: Spatial-temporal Refinement for Consistent Semantic Segmentation—0
The Instance-centric Transformer for the RVOS Track of LSVOS Challenge: 3rd Place Solution—0
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation—0
THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation—0
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping—0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut—0
Towards Good Practices for Video Object Segmentation—0
Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and Ensembles—0
Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation—0
Training-Free Robust Interactive Video Object Segmentation—0
TrickVOS: A Bag of Tricks for Video Object Segmentation—0
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
TubeFormer-DeepLab: Video Mask Transformer—0
Two-Stream Networks for Object Segmentation in Videos—0
Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability—0
Understanding Video Transformers via Universal Concept Discovery—0
U-Net Based Multi-instance Video Object Segmentation—0
UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track—0
Unsupervised Domain Adaptation for Video Semantic Segmentation—0
Unsupervised RGBD Video Object Segmentation Using GANs—0
Unsupervised Semantic Scene Labeling for Streaming Data—0
Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration—0
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation—0
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation—0
Unsupervised Video Object Segmentation with Motion-based Bilateral Networks—0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking—0
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning—0
Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning—0
Value of Temporal Dynamics Information in Driving Scene Segmentation—0
VideoClick: Video Object Segmentation with a Single Click—0
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All datasetsCityscapes valCamVidVSPWLaRSMultispectral Video Semantic Segmentation

Benchmark Results

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