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 701–750 of 895 papers

TitleStatusHype
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions—0
Video Object Segmentation Using Global and Instance Embedding Learning—0
Video Object Segmentation using Tracked Object Proposals—0
Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track—0
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation—0
Video Object Segmentation with Language Referring Expressions—0
Video Object Segmentation Without Temporal Information—0
Video Propagation Networks—0
Video Salient Object Detection Using Spatiotemporal Deep Features—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
VideoSAM: Open-World Video Segmentation—0
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network—0
Video Segmentation via Diffusion Bases—0
Video Segmentation via Multiple Granularity Analysis—0
Video Segmentation via Object Flow—0
Video Segmentation With Just a Few Strokes—0
VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models—0
VidSeg: Training-free Video Semantic Segmentation based on Diffusion Models—0
ViSeRet: A simple yet effective approach to moment retrieval via fine-grained video segmentation—0
Visual Representation Learning with Stochastic Frame Prediction—0
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview—0
Visual Subtitle Feature Enhanced Video Outline Generation—0
Visual-Textual Capsule Routing for Text-Based Video Segmentation—0
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation—0
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos—0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
Weakly Supervised Multiclass Video Segmentation—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset—0
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark—0
Zero-Shot 4D Lidar Panoptic Segmentation—0
Zero-shot capability of SAM-family models for bone segmentation in CT scans—0
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models—0
ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation—0
VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal—0
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation—0
Region Aware Video Object Segmentation with Deep Motion Modeling—0
Region-based Particle Filter for Video Object Segmentation—0
Reinforcement Cutting-Agent Learning for Video Object Segmentation—0
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation—0
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking Video Segmentation with Masked Video Consistency: Did the Model Learn as Intended?—0
RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory—0
RMem: Restricted Memory Banks Improve Video Object Segmentation—0
Robust and Efficient Memory Network for Video Object Segmentation—0
Robust Referring Video Object Segmentation with Cyclic Structural Consensus—0
Robust Visual Tracking by Motion Analyzing—0
Role of the Pretraining and the Adaptation data sizes for low-resource real-time MRI video segmentation—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—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