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 251–300 of 895 papers

TitleStatusHype
Lester: rotoscope animation through video object segmentation and trackingCode1
Isomer: Isomerous Transformer for Zero-shot Video Object SegmentationCode1
LVOS: A Benchmark for Long-term Video Object SegmentationCode1
Fast Template Matching and Update for Video Object Tracking and SegmentationCode1
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
Learning Object Depth from Camera Motion and Video Object SegmentationCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
Language-Bridged Spatial-Temporal Interaction for Referring Video Object SegmentationCode1
Learning Fast and Robust Target Models for Video Object SegmentationCode1
Learning Quality-aware Dynamic Memory for Video Object SegmentationCode1
Fast Video Object Segmentation using the Global Context ModuleCode1
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object SegmentationCode1
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video SegmentationCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
Kernelized Memory Network for Video Object SegmentationCode1
Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural NetworksCode1
Global Spectral Filter Memory Network for Video Object SegmentationCode1
1st Place Solution for 5th LSVOS Challenge: Referring Video Object SegmentationCode1
Flow-based Video Segmentation for Human Head and ShouldersCode1
Local-Global Context Aware Transformer for Language-Guided Video SegmentationCode1
High Temporal Consistency through Semantic Similarity Propagation in Semi-Supervised Video Semantic Segmentation for Autonomous FlightCode1
M^3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object SegmentationCode1
Learning to Learn Better for Video Object SegmentationCode1
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object SegmentationCode1
M^3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object SegmentationCode1
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic SegmentationCode1
Multi-Granularity Video Object SegmentationCode1
Guided Interactive Video Object Segmentation Using Reliability-Based Attention MapsCode1
Referring Video Object Segmentation via Language-aligned Track SelectionCode1
Temporal Aggregate Representations for Long-Range Video UnderstandingCode1
Fast Sprite Decomposition from Animated Graphics—0
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation—0
Is SAM 2 Better than SAM in Medical Image Segmentation?—0
Is Segment Anything Model 2 All You Need for Surgery Video Segmentation? A Systematic Evaluation—0
Fast Action Proposals for Human Action Detection and Search—0
CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF—0
Real-Time Segmentation Networks should be Latency Aware—0
ISEC: Iterative over-Segmentation via Edge Clustering—0
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound—0
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation—0
Eye Tracking Assisted Extraction of Attentionally Important Objects From Videos—0
An end-to-end generative framework for video segmentation and recognition—0
Investigation of Frame Differences as Motion Cues for Video Object Segmentation—0
Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation—0
ClickTrack: Towards Real-time Interactive Single Object Tracking—0
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow—0
An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos—0
InterRVOS: Interaction-aware Referring Video Object Segmentation—0
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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