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 601–650 of 895 papers

TitleStatusHype
SeamSeg: Video Object Segmentation using Patch Seams—0
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction—0
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering—0
SegGPT: Towards Segmenting Everything in Context—0
Segment Every Reference Object in Spatial and Temporal Spaces—0
Selective Video Object Cutout—0
Self-Occlusions and Disocclusions in Causal Video Object Segmentation—0
Self-supervised Motion Representation via Scattering Local Motion Cues—0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention—0
Self-supervised Video Object Segmentation by Motion Grouping—0
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging—0
Semantically-Guided Video Object Segmentation—0
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks—0
Semantic and Sequential Alignment for Referring Video Object Segmentation—0
Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models—0
Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach—0
Semantic Video Segmentation: A Review on Recent Approaches—0
Semantic Video Segmentation by Gated Recurrent Flow Propagation—0
Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures—0
Semantic video segmentation for autonomous driving—0
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—0
Sequential Clique Optimization for Video Object Segmentation—0
Shift and matching queries for video semantic segmentation—0
Shifted Chunk Transformer for Spatio-Temporal Representational Learning—0
Siamese Masked Autoencoders—0
SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation—0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation—0
Sketch-based Video Object Segmentation: Benchmark and Analysis—0
SOLD: Sub-Optimal Low-rank Decomposition for Efficient Video Segmentation—0
Spacetime Graph Optimization for Video Object Segmentation—0
Space Time Recurrent Memory Network—0
Space-time Reinforcement Network for Video Object Segmentation—0
Spatial-Temporal Multi-level Association for Video Object Segmentation—0
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation—0
Spatio-Temporal Image Boundary Extrapolation—0
Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation—0
SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objects in Weakly Labelled Videos—0
SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution—0
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—0
Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation—0
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications—0
STSeg-Complex Video Object Segmentation: The 1st Solution for 4th PVUW MOSE Challenge—0
Submodular video object proposal selection for semantic object segmentation—0
Superframes, A Temporal Video Segmentation—0
Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior—0
Super-Trajectory for Video Segmentation—0
Symmetric block-low-rank layers for fully reversible multilevel neural networks—0
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation—0
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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