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 501–550 of 895 papers

TitleStatusHype
Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection—0
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation—0
Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Learning Position and Target Consistency for Memory-based Video Object Segmentation—0
Learning Referring Video Object Segmentation from Weak Annotation—0
Learning Representations from Audio-Visual Spatial Alignment—0
Learning Spatial-Semantic Features for Robust Video Object Segmentation—0
Learning the What and How of Annotation in Video Object Segmentation—0
Learning to Adapt to Online Streams with Distribution Shifts—0
Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos—0
Learning To Segment Dominant Object Motion From Watching Videos—0
Learning to Segment Human by Watching YouTube—0
Learning to Segment Moving Objects in Videos—0
Learning to Segment Referred Objects from Narrated Egocentric Videos—0
Learning to Sort Image Sequences via Accumulated Temporal Differences—0
Learning to Track Any Object—0
Learning Video Object Segmentation with Visual Memory—0
Leveraging Motion Information for Better Self-Supervised Video Correspondence Learning—0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
LIP: Learning Instance Propagation for Video Object Segmentation—0
Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation—0
Look Before You Match: Instance Understanding Matters in Video Object Segmentation—0
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes—0
Low-Latency Video Semantic Segmentation—0
LSM: Learning Subspace Minimization for Low-level Vision—0
LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS—0
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation—0
TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking—0
MAC-DO: An Efficient Output-Stationary GEMM Accelerator for CNNs Using DRAM Technology—0
MAIN: Multi-Attention Instance Network for Video Segmentation—0
MAS for video objects segmentation and tracking based on active contours and SURF descriptor—0
Mask Propagation Network for Video Object Segmentation—0
MaskRNN: Instance Level Video Object Segmentation—0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation—0
MEDIAPI-SKEL - A 2D-Skeleton Video Database of French Sign Language With Aligned French Subtitles—0
MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer—0
MEGA: Multimodal Alignment Aggregation and Distillation For Cinematic Video Segmentation—0
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation—0
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation—0
Memory-Efficient Continual Learning Object Segmentation for Long Video—0
Memory Matching is not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation—0
Memory Selection Network for Video Propagation—0
MeNToS: Tracklets Association with a Space-Time Memory Network—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
Mining Minimal Map-Segments for Visual Place Classifiers—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation—0
MoNet: Deep Motion Exploitation for Video Object Segmentation—0
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection—0
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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