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 401–450 of 895 papers

TitleStatusHype
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation—0
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation—0
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs—0
Discriminative Online Learning for Fast Video Object Segmentation—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation—0
Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation—0
Dual Temporal Memory Network for Efficient Video Object Segmentation—0
A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation—0
Dynamic Face Video Segmentation via Reinforcement Learning—0
Dynamic Video Segmentation Network—0
Efficient Heterogeneous Video Segmentation at the Edge—0
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation—0
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters—0
Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priors—0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance—0
Efficient Video Segmentation Models with Per-frame Inference—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
Efficient Video Semantic Segmentation with Labels Propagation and Refinement—0
EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle—0
End to End Video Segmentation for Driving : Lane Detection For Autonomous Car—0
EntitySAM: Segment Everything in Video—0
Event-guided Low-light Video Semantic Segmentation—0
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow—0
Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation—0
Eye Tracking Assisted Extraction of Attentionally Important Objects From Videos—0
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation—0
Real-Time Segmentation Networks should be Latency Aware—0
Fast Action Proposals for Human Action Detection and Search—0
Fast Sprite Decomposition from Animated Graphics—0
Fast Video Object Segmentation via Dynamic Targeting Network—0
Fast Video Object Segmentation via Mask Transfer Network—0
Fast video object segmentation with Spatio-Temporal GANs—0
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching—0
FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching—0
Flow-free Video Object Segmentation—0
Flow-guided Semi-supervised Video Object Segmentation—0
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation—0
FODVid: Flow-guided Object Discovery in Videos—0
FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models—0
FoodMem: Near Real-time and Precise Food Video Segmentation—0
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography—0
Fully Connected Object Proposals for Video Segmentation—0
Fully Hyperbolic Convolutional Neural Networks—0
Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation—0
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos—0
FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos—0
FVOS for MOSE Track of 4th PVUW Challenge: 3rd Place Solution—0
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All datasetsCityscapes valCamVidVSPWLaRSMultispectral Video Semantic Segmentation

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TMANet-50mIoU80.3—Unverified
2DeltaDist-DDRNet-39mIoU79.9—Unverified
3TDNet-50 [9]mIoU79.9—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