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 301–350 of 895 papers

TitleStatusHype
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild—0
MASSeg : 2nd Technical Report for 4th PVUW MOSE TrackCode0
FVOS for MOSE Track of 4th PVUW Challenge: 3rd Place Solution—0
Multi-person Physics-based Pose Estimation for Combat Sports—0
STSeg-Complex Video Object Segmentation: The 1st Solution for 4th PVUW MOSE Challenge—0
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation—0
Zero-Shot 4D Lidar Panoptic Segmentation—0
CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection—0
ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025Code0
Online Reasoning Video Segmentation with Just-in-Time Digital Twins—0
Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation—0
One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory NetworksCode0
Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency RelearningCode0
Leveraging Vision-Language Models for Open-Vocabulary Instance Segmentation and TrackingCode0
AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations—0
SAM2 for Image and Video Segmentation: A Comprehensive Survey—0
Leveraging Motion Information for Better Self-Supervised Video Correspondence Learning—0
Investigation of Frame Differences as Motion Cues for Video Object Segmentation—0
Open-World Skill Discovery from Unsegmented Demonstrations—0
OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation—0
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching—0
Parameter-free Video Segmentation for Vision and Language Understanding—0
An Analysis of Data Transformation Effects on Segment Anything 2—0
Deep learning approaches to surgical video segmentation and object detection: A Scoping Review—0
Pointmap Association and Piecewise-Plane Constraint for Consistent and Compact 3D Gaussian Segmentation Field—0
Role of the Pretraining and the Adaptation data sizes for low-resource real-time MRI video segmentation—0
Wandering around: A bioinspired approach to visual attention through object motion sensitivityCode0
HD-EPIC: A Highly-Detailed Egocentric Video Dataset—0
Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priors—0
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations—0
Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video SegmentationCode0
Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation—0
Multi-Context Temporal Consistent Modeling for Referring Video Object SegmentationCode0
Segment Anything Model for Zero-shot Single Particle Tracking in Liquid Phase Transmission Electron MicroscopyCode0
VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
EntitySAM: Segment Everything in Video—0
DTOS: Dynamic Time Object Sensing with Large Multimodal ModelCode0
Decoupled Motion Expression Video Segmentation—0
VidSeg: Training-free Video Semantic Segmentation based on Diffusion Models—0
Semantic and Sequential Alignment for Referring Video Object Segmentation—0
Is Segment Anything Model 2 All You Need for Surgery Video Segmentation? A Systematic Evaluation—0
Generative Video Propagation—0
When SAM2 Meets Video Shadow and Mirror DetectionCode0
Collaborative Hybrid Propagator for Temporal Misalignment in Audio-Visual Segmentation—0
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—0
Stable Mean Teacher for Semi-supervised Video Action DetectionCode0
Video Decomposition Prior: A Methodology to Decompose Videos into Layers—0
Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
ClickTrack: Towards Real-time Interactive Single Object Tracking—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