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 401–450 of 895 papers

TitleStatusHype
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object DetectionCode0
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
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
3rd Place Solution for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression guided Video Segmentation—0
A Semi-Self-Supervised Approach for Dense-Pattern Video Object 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
Automatic Dance Video Segmentation for Understanding Choreography—0
MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and MotionCode0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
One-shot Training for Video Object Segmentation—0
Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation—0
Global Motion Understanding in Large-Scale Video Object Segmentation—0
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation—0
Space-time Reinforcement Network for Video Object Segmentation—0
360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos—0
arcjetCV: an open-source software to analyze material ablationCode0
Spatial-Temporal Multi-level Association for Video Object Segmentation—0
Annolid: Annotate, Segment, and Track Anything You NeedCode0
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components—0
OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework—0
ClickVOS: Click Video Object SegmentationCode0
Deep Common Feature Mining for Efficient Video Semantic SegmentationCode0
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation—0
PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTMCode0
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
Point-VOS: Pointing Up Video Object Segmentation—0
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound—0
Vanishing-Point-Guided Video Semantic Segmentation of Driving ScenesCode0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention—0
Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation—0
Understanding Video Transformers via Universal Concept Discovery—0
Infer from What You Have Seen Before: Temporally-dependent Classifier for Semi-supervised Video SegmentationCode0
Learning to Segment Referred Objects from Narrated Egocentric Videos—0
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision—0
Appearance-Based Refinement for Object-Centric Motion Segmentation—0
Artificial intelligence optical hardware empowers high-resolution hyperspectral video understanding at 1.2 Tb/s—0
Hierarchical Graph Pattern Understanding for Zero-Shot VOSCode0
TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking—0
Semi-supervised Active Learning for Video Action DetectionCode0
Flexible visual prompts for in-context learning in computer visionCode0
GenDeF: Learning Generative Deformation Field for Video Generation—0
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception—0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models—0
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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