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 151–200 of 895 papers

TitleStatusHype
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
SAM 2: Segment Anything in Images and VideosCode12
Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object SegmentationCode0
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical videoCode0
ViLLa: Video Reasoning Segmentation with Large Language ModelCode1
FoodMem: Near Real-time and Precise Food Video Segmentation—0
VISA: Reasoning Video Object Segmentation via Large Language ModelsCode3
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Learning Spatial-Semantic Features for Robust Video Object Segmentation—0
ActionVOS: Actions as Prompts for Video Object SegmentationCode1
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
General and Task-Oriented Video SegmentationCode1
Submodular video object proposal selection for semantic object segmentation—0
Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation—0
Context Propagation from Proposals for Semantic Video Object Segmentation—0
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-ResolutionCode0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
Video Inpainting Localization with Contrastive LearningCode1
PVUW 2024 Challenge on Complex Video Understanding: Methods and ResultsCode4
Multimodal Segmentation for Vocal Tract Modeling—0
2nd Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation—0
SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp SegmentationCode1
Trusted Video Inpainting Localization via Deep Attentive Noise LearningCode0
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object DetectionCode0
GroPrompt: Efficient Grounded Prompting and Adaptation for Referring Video Object Segmentation—0
2nd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation—0
RMem: Restricted Memory Banks Improve Video Object Segmentation—0
Visual Representation Learning with Stochastic Frame Prediction—0
1st Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video SegmentationCode1
I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data—0
Training-Free Robust Interactive Video Object Segmentation—0
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation—0
1st Place Solution for MOSE Track in CVPR 2024 PVUW Workshop: Complex Video Object Segmentation—0
A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation—0
3rd Place Solution for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression guided Video Segmentation—0
3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex Video Object Segmentation—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—0
MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and MotionCode0
Automatic Dance Video Segmentation for Understanding Choreography—0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion ModelsCode2
One-shot Training for Video Object Segmentation—0
Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation—0
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation—0
Global Motion Understanding in Large-Scale Video Object Segmentation—0
Space-time Reinforcement Network for Video Object Segmentation—0
LVOS: A Benchmark for Large-scale Long-term Video Object SegmentationCode2
360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos—0
Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object SegmentationCode2
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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