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 101–150 of 895 papers

TitleStatusHype
Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video SegmentationCode3
Geometric Algebra Planes: Convex Implicit Neural Volumes—0
ClickTrack: Towards Real-time Interactive Single Object Tracking—0
IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet VideosCode2
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level—0
Zero-shot capability of SAM-family models for bone segmentation in CT scans—0
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection DataCode0
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting—0
Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters—0
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
LiVOS: Light Video Object Segmentation with Gated Linear MatchingCode1
Event-guided Low-light Video Semantic Segmentation—0
Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI SegmentationCode0
Addressing Issues with Working Memory in Video Object Segmentation—0
SMITE: Segment Me In TimECode3
VideoSAM: A Large Vision Foundation Model for High-Speed Video SegmentationCode0
SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory TreeCode4
Temporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation—0
Configurable Embodied Data Generation for Class-Agnostic RGB-D Video Segmentation—0
VideoSAM: Open-World Video Segmentation—0
Shift and matching queries for video semantic segmentation—0
One Token to Seg Them All: Language Instructed Reasoning Segmentation in VideosCode2
X-Prompt: Multi-modal Visual Prompt for Video Object SegmentationCode1
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2Code5
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation—0
Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision—0
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 ModelCode2
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation—0
Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOSCode0
Unleashing the Temporal-Spatial Reasoning Capacity of GPT for Training-Free Audio and Language Referenced Video Object SegmentationCode2
CSS-Segment: 2nd Place Report of LSVOS Challenge VOS Track—0
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A SurveyCode5
The 2nd Solution for LSVOS Challenge RVOS Track: Spatial-temporal Refinement for Consistent Semantic Segmentation—0
The Instance-centric Transformer for the RVOS Track of LSVOS Challenge: 3rd Place Solution—0
Rethinking Video Segmentation with Masked Video Consistency: Did the Model Learn as Intended?—0
LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS—0
Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track—0
3D-Aware Instance Segmentation and Tracking in Egocentric Videos—0
UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track—0
Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame PruningCode2
Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2—0
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation—0
Saliency Detection in Educational Videos: Analyzing the Performance of Current Models, Identifying Limitations and Advancement Directions—0
Is SAM 2 Better than SAM in Medical Image Segmentation?—0
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation—0
Fast Sprite Decomposition from Animated Graphics—0
Segment Anything in Medical Images and Videos: Benchmark and DeploymentCode7
Biomedical SAM 2: Segment Anything in Biomedical Images and VideosCode0
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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