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 451–500 of 895 papers

TitleStatusHype
An Image Processing Pipeline for Camera Trap Time-Lapse RecordingsCode0
Language-Bridged Spatial-Temporal Interaction for Referring Video Object SegmentationCode1
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic InformationCode1
A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages—0
Differentiable Soft-Masked AttentionCode1
TubeFormer-DeepLab: Video Mask Transformer—0
Collaborative Attention Memory Network for Video Object Segmentation—0
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion—0
Recurrent Dynamic Embedding for Video Object SegmentationCode1
Boosting Video Object Segmentation based on Scale InconsistencyCode0
Representation Recycling for Streaming Video AnalysisCode0
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging—0
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition—0
Adaptive Memory Management for Video Object SegmentationCode0
Video K-Net: A Simple, Strong, and Unified Baseline for Video SegmentationCode1
Learning Local and Global Temporal Contexts for Video Semantic SegmentationCode1
Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationCode1
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
Human Instance Segmentation and Tracking via Data Association and Single-stage Detector—0
Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation—0
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools SegmentationCode3
In-N-Out Generative Learning for Dense Unsupervised Video SegmentationCode1
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation—0
Temporal Transductive Inference for Few-Shot Video Object SegmentationCode0
Scalable Video Object Segmentation with Identification MechanismCode2
Robust Visual Tracking by SegmentationCode1
Local-Global Context Aware Transformer for Language-Guided Video SegmentationCode1
Delta Distillation for Efficient Video ProcessingCode0
Object discovery and representation networksCode0
Temporal Context for Robust Maritime Obstacle DetectionCode1
End-to-End Semi-Supervised Learning for Video Action DetectionCode1
RankSeg: Adaptive Pixel Classification with Image Category Ranking for SegmentationCode1
Revisiting Click-based Interactive Video Object SegmentationCode0
Efficient Video Segmentation Models with Per-frame Inference—0
Box Supervised Video Segmentation Proposal NetworkCode0
Borrowing from yourself: Faster future video segmentation with partial channel updateCode0
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Language as Queries for Referring Video Object SegmentationCode2
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device—0
Semi-Supervised Video Semantic Segmentation With Inter-Frame Feature ReconstructionCode1
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset—0
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation—0
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising NetworksCode1
Siamese Network with Interactive Transformer for Video Object SegmentationCode0
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation—0
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier—0
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking—0
Mask2Former for Video Instance SegmentationCode2
HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static ImagesCode1
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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