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

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Showing 251–300 of 895 papers

TitleStatusHype
Video Panoptic SegmentationCode1
Video Semantic Segmentation with Distortion-Aware Feature CorrectionCode1
Real-Time Video Inference on Edge Devices via Adaptive Model StreamingCode1
Temporal Aggregate Representations for Long-Range Video UnderstandingCode1
Physarum Powered Differentiable Linear Programming Layers and ApplicationsCode1
Fast Template Matching and Update for Video Object Tracking and SegmentationCode1
A Transductive Approach for Video Object SegmentationCode1
Temporally Distributed Networks for Fast Video Semantic SegmentationCode1
TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain KnowledgeCode1
Learning What to Learn for Video Object SegmentationCode1
Collaborative Video Object Segmentation by Foreground-Background IntegrationCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
Motion-Attentive Transition for Zero-Shot Video Object SegmentationCode1
State-Aware Tracker for Real-Time Video Object SegmentationCode1
Learning Fast and Robust Target Models for Video Object SegmentationCode1
Efficient Semantic Video Segmentation with Per-frame InferenceCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
Directional Deep Embedding and Appearance Learning for Fast Video Object SegmentationCode1
Fast Video Object Segmentation using the Global Context ModuleCode1
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese NetworksCode1
Zero-Shot Video Object Segmentation via Attentive Graph Neural NetworksCode1
UnOVOST: Unsupervised Offline Video Object Segmentation and TrackingCode1
Separable Convolutional LSTMs for Faster Video SegmentationCode1
Semantic Segmentation of Video Sequences with Convolutional LSTMsCode1
Video Object Segmentation using Space-Time Memory NetworksCode1
Online Model Distillation for Efficient Video InferenceCode1
Tukey-Inspired Video Object SegmentationCode1
YouTube-VOS: Sequence-to-Sequence Video Object SegmentationCode1
Actor and Action Video Segmentation from a SentenceCode1
Pyramid Scene Parsing NetworkCode1
Deep Feature Flow for Video RecognitionCode1
Clockwork Convnets for Video Semantic SegmentationCode1
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction—0
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation—0
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation—0
CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video—0
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment—0
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects—0
Q-SAM2: Accurate Quantization for Segment Anything Model 2—0
THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation—0
InterRVOS: Interaction-aware Referring Video Object Segmentation—0
OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall DetectionCode0
ThinkVideo: High-Quality Reasoning Video Segmentation with Chain of ThoughtsCode0
Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation—0
FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching—0
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation—0
6D Pose Estimation on Spoons and Hands—0
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection—0
RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory—0
Few-Shot Referring Video Single- and Multi-Object Segmentation via Cross-Modal Affinity with Instance Sequence MatchingCode0
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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