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 751–800 of 895 papers

TitleStatusHype
OVSNet : Towards One-Pass Real-Time Video Object Segmentation—0
Parameter-free Video Segmentation for Vision and Language Understanding—0
Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams—0
Perceptual Consistency in Video Segmentation—0
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation—0
Pixel-Level Equalized Matching for Video Object Segmentation—0
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks—0
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos—0
PMVOS: Pixel-Level Matching-Based Video Object Segmentation—0
Pointmap Association and Piecewise-Plane Constraint for Consistent and Compact 3D Gaussian Segmentation Field—0
Point-VOS: Pointing Up Video Object Segmentation—0
Point-wise mutual information-based video segmentation with high temporal consistency—0
Prediction-Tracking-Segmentation—0
Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions—0
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow—0
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild—0
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection—0
Q-SAM2: Accurate Quantization for Segment Anything Model 2—0
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks—0
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework—0
R-Clustering for Egocentric Video Segmentation—0
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device—0
Real-time Localized Photorealistic Video Style Transfer—0
ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation—0
Recurrent Fully Convolutional Networks for Video Segmentation—0
Recurrent Segmentation for Variable Computational Budgets—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
Reducing the Annotation Effort for Video Object Segmentation Datasets—0
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations—0
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network—0
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation—0
Region Aware Video Object Segmentation with Deep Motion Modeling—0
Region-based Particle Filter for Video Object Segmentation—0
Reinforcement Cutting-Agent Learning for Video Object Segmentation—0
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation—0
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking Video Segmentation with Masked Video Consistency: Did the Model Learn as Intended?—0
RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory—0
RMem: Restricted Memory Banks Improve Video Object Segmentation—0
Robust and Efficient Memory Network for Video Object Segmentation—0
Robust Referring Video Object Segmentation with Cyclic Structural Consensus—0
Robust Visual Tracking by Motion Analyzing—0
Role of the Pretraining and the Adaptation data sizes for low-resource real-time MRI video segmentation—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation—0
SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation - A Synthetic Dataset and Baselines—0
Saliency-Aware Geodesic Video Object Segmentation—0
Saliency Detection in Educational Videos: Analyzing the Performance of Current Models, Identifying Limitations and Advancement Directions—0
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation—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