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

Showing 150 of 895 papers

TitleStatusHype
SAM 2: Segment Anything in Images and VideosCode11
Segment Anything in Medical Images and Videos: Benchmark and DeploymentCode7
Efficient Track AnythingCode7
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and VideosCode5
OMG-Seg: Is One Model Good Enough For All Segmentation?Code5
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2Code5
4th PVUW MeViS 3rd Place Report: Sa2VACode5
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A SurveyCode5
The 1st Solution for 4th PVUW MeViS Challenge: Unleashing the Potential of Large Multimodal Models for Referring Video SegmentationCode5
SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory TreeCode4
MedSAM2: Segment Anything in 3D Medical Images and VideosCode4
SegGPT: Segmenting Everything In ContextCode4
EdgeTAM: On-Device Track Anything ModelCode4
SiamMask: A Framework for Fast Online Object Tracking and SegmentationCode4
PVUW 2024 Challenge on Complex Video Understanding: Methods and ResultsCode4
SMITE: Segment Me In TimECode3
SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video SegmentationCode3
RAP-SAM: Towards Real-Time All-Purpose Segment AnythingCode3
Personalize Segment Anything Model with One ShotCode3
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory ModelCode3
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools SegmentationCode3
VideoCutLER: Surprisingly Simple Unsupervised Video Instance SegmentationCode3
PSALM: Pixelwise SegmentAtion with Large Multi-Modal ModelCode3
Putting the Object Back into Video Object SegmentationCode3
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different ScenesCode3
VISA: Reasoning Video Object Segmentation via Large Language ModelsCode3
UniVS: Unified and Universal Video Segmentation with Prompts as QueriesCode3
Tracking Anything with Decoupled Video SegmentationCode3
Moving Object Segmentation: All You Need Is SAM (and Flow)Code3
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 ModelCode2
Audio-Visual Segmentation with SemanticsCode2
Decoupling Features in Hierarchical Propagation for Video Object SegmentationCode2
Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame PruningCode2
MOSE: A New Dataset for Video Object Segmentation in Complex ScenesCode2
Scalable Video Object Segmentation with Identification MechanismCode2
MeViS: A Large-scale Benchmark for Video Segmentation with Motion ExpressionsCode2
Mask2Former for Video Instance SegmentationCode2
Language as Queries for Referring Video Object SegmentationCode2
InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language ModelsCode2
LVOS: A Benchmark for Large-scale Long-term Video Object SegmentationCode2
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic SegmentationCode2
MemSAM: Taming Segment Anything Model for Echocardiography Video SegmentationCode2
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System CollaborationCode2
IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet VideosCode2
HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual PerceiverCode2
In Defense of Online Models for Video Instance SegmentationCode2
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video SegmentationCode2
Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object SegmentationCode2
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any GranularityCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TMANet-50mIoU80.3Unverified
2TDNet-50 [9]mIoU79.9Unverified
3DeltaDist-DDRNet-39mIoU79.9Unverified
4PSPNet-101 [20]mIoU79.7Unverified
5PSPNet-50 [20]mIoU78.1Unverified
6LVS [12]mIoU76.8Unverified
7GRFP [15]mIoU73.6Unverified
8FCN-50 [14]mIoU70.1Unverified
9DFF [22]mIoU69.2Unverified
#ModelMetricClaimedVerifiedStatus
1TMANet-50Mean IoU76.5Unverified
2ETC-MobileNetMean IoU76.3Unverified
3TDNet-50Mean IoU76.2Unverified
4PSPNet-50Mean IoU76Unverified
5NetwarpMean IoU74.7Unverified
6GRFPMean IoU67.1Unverified
#ModelMetricClaimedVerifiedStatus
1DVIS++(VIT-L)mIoU63.8Unverified
2UniVS(Swin-L)mIoU59.8Unverified
3Tube-Link(Swin-large)mIoU59.6Unverified
4MRCFA(MiT-B5)mIoU49.9Unverified
5CFFM(MiT-B5)mIoU49.3Unverified
#ModelMetricClaimedVerifiedStatus
1WaSR-T (ResNet-101)Q60.1Unverified
2TMANet (ResNet-50)Q57.5Unverified
3CSANet (ResNet-101)Q49.1Unverified
#ModelMetricClaimedVerifiedStatus
1MVNet(DeepLabV3)mIoU54.52Unverified
2MVNet(PSPNet)mIoU54.36Unverified
3MVNet(FCN)mIoU53.9Unverified