https://github.com/0liliulei/mask-vos
공식 코드Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation
Papers with Code 아카이브
Video Semantic Segmentation
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/594422814/ContrastCorr
공식 코드Contrastive Transformation for Self-supervised Correspondence Learning
https://github.com/983632847/awesome-multimodal-object-tracking
공식 코드Underwater Camouflaged Object Tracking Meets Vision-Language SAM2
https://github.com/AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection
공식 코드End-to-End Semi-Supervised Learning for Video Action Detection
https://github.com/AKASH2907/stable-mean-teacher
공식 코드Stable Mean Teacher for Semi-supervised Video Action Detection
https://github.com/AVC2-UESTC/OV2VSS
공식 코드https://github.com/Ali2500/HODOR
공식 코드HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images
https://github.com/Ali2500/TarViS
공식 코드TarViS: A Unified Approach for Target-based Video Segmentation
https://github.com/Andreas-Pfeuffer/LSTM-ICNet
공식 코드https://github.com/AngeLouCN/SAM-2_Surgical_Video
공식 코드Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2
https://github.com/Dayan-Guan/DA-VSN
공식 코드Domain Adaptive Video Segmentation via Temporal Consistency Regularization
https://github.com/GewelsJI/FSNet
공식 코드https://github.com/GiuliaDAngelo/Speckegomotion
공식 코드Wandering around: A bioinspired approach to visual attention through object motion sensitivity
https://github.com/HCPLab-SYSU/ATEN
공식 코드Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
https://github.com/HYOJINPARK/Reuse_VOS
공식 코드Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
https://github.com/HYOJINPARK/TTVOS
공식 코드https://github.com/HeatherJiaZG/SuperGlue-pytorch
공식 코드Physarum Powered Differentiable Linear Programming Layers and Applications
https://github.com/HengyiWang/SIRNet
공식 코드Boosting Video Object Segmentation based on Scale Inconsistency
https://github.com/Hydragon516/PMN
공식 코드Unsupervised Video Object Segmentation via Prototype Memory Network
https://github.com/JingchunCheng/FAVOS
공식 코드Fast and Accurate Online Video Object Segmentation via Tracking Parts
https://github.com/JingchunCheng/SegFlow
공식 코드SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
https://github.com/Jyxarthur/flowsam
공식 코드https://github.com/KevinDuarte/CapsuleVOS
공식 코드CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing
https://github.com/LingyiHongfd/LVOS
공식 코드https://github.com/MECLabTUDA/SASVi
공식 코드https://github.com/MSiam/AdaptiveMaskedProxies
공식 코드https://github.com/MSiam/motion_adaptation
공식 코드https://github.com/MegviiDetection/video_analyst
공식 코드https://github.com/MohsenFayyaz89/STFCN
공식 코드https://github.com/NUST-Machine-Intelligence-Laboratory/HFAN
공식 코드Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
https://github.com/NiFangBaAGe/DATTT
공식 코드Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
https://github.com/PaddlePaddle/PaddleSeg
공식 코드EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle
https://github.com/QianWangX/VidSeg_diffusion
공식 코드Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models
https://github.com/Qualcomm-AI-research/delta-distillation
공식 코드https://github.com/RobertLuo1/NeurIPS2023_SOC
공식 코드SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
https://github.com/SHI-Labs/Mask-Selection-Networks
공식 코드MSN: Efficient Online Mask Selection Network for Video Instance Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.