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 701–750 of 895 papers

TitleStatusHype
Memory-Efficient Continual Learning Object Segmentation for Long Video—0
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation—0
Memory Selection Network for Video Propagation—0
MeNToS: Tracklets Association with a Space-Time Memory Network—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
Mining Minimal Map-Segments for Visual Place Classifiers—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation—0
MoNet: Deep Motion Exploitation for Video Object Segmentation—0
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection—0
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation—0
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level—0
Motion-Guided Cascaded Refinement Network for Video Object Segmentation—0
Motion-inductive Self-supervised Object Discovery in Videos—0
Motion Prediction in Visual Object Tracking—0
Motion-state Alignment for Video Semantic Segmentation—0
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
Moving Object Segmentation in Jittery Videos by Stabilizing Trajectories Modeled in Kendall's Shape Space—0
MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation—0
Multiclass Semantic Video Segmentation With Object-Level Active Inference—0
Multi-class Video Co-segmentation with a Generative Multi-video Model—0
Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation—0
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation—0
Multi-modal Capsule Routing for Actor and Action Video Segmentation Conditioned on Natural Language Queries—0
Multimodal Segmentation for Vocal Tract Modeling—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
Multi-person Physics-based Pose Estimation for Combat Sports—0
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations—0
Multi-stream CNN based Video Semantic Segmentation for Automated Driving—0
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation—0
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation—0
NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning—0
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation—0
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision—0
Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation—0
Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2—0
Novel tile segmentation scheme for omnidirectional video—0
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation—0
Object Segmentation Tracking from Generic Video Cues—0
Object Segmentation with Audio Context—0
OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation—0
One-shot Training for Video Object Segmentation—0
One-Shot Video Inpainting—0
One-Shot Weakly Supervised Video Object Segmentation—0
OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework—0
On guiding video object segmentation—0
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation—0
Online Reasoning Video Segmentation with Just-in-Time Digital Twins—0
Online Video Object Segmentation via Convolutional Trident Network—0
Open-World Skill Discovery from Unsegmented Demonstrations—0
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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