Papers with Code 아카이브
Video Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/DaiLisen/Eye-gaze-Point-Detection-Modified-sec-
https://github.com/DiMarzioRock7/Aerial-Semantic-Segmentation-Drone
https://github.com/DiMarzioRock7/PSPNet
https://github.com/DoTung-bkhn/multiclass-segmentation
https://github.com/DrBoltzmann/CarND-Semantic-Segmentation
https://github.com/EquipoVandV/mmactionVandV
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/EvaBr/AnatomyNets
https://github.com/FaceOnLive/Realtime-Background-Changer-SDK-Android
UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces
https://github.com/GCVCG/VolETA-MetaFood
XMem++: Production-level Video Segmentation From Few Annotated Frames
https://github.com/GLUS-video/GLUS
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
https://github.com/HenonBamboo/PSPNet-MindSpore
https://github.com/IDEA-Research/Grounded-Segment-Anything
https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models
https://github.com/JerryX1110/awesome-rvos
https://github.com/JonathonLuiten/PReMVOS
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
https://github.com/JunLi-Galios/CDFL
Weakly Supervised Energy-Based Learning for Action Segmentation
https://github.com/LiwenWang919/M3-UDA
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/Lxrd-AJ/Advanced_ML
https://github.com/MS-Mind/MS-Code-06/tree/main/PSPNet
https://github.com/Media-Smart/vedaseg
https://github.com/Mind23-2/MindCode-5/tree/main/PSPNet
https://github.com/Mind23-2/MindCode-63
https://github.com/MindCode-4/code-4/tree/main/sam
https://github.com/MindSpore-paper-code-2/code2/tree/main/OSVOS
https://github.com/MindSpore-paper-code-2/code2/tree/main/PSPNet
https://github.com/MindSpore-paper-code-3/code10/tree/main/FCN8s
https://github.com/MindSpore-scientific/code-7/tree/main/SAM
https://github.com/Osdel/ssnets
https://github.com/PRBonn/bonnet
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/segmentation/cfbi.md
Collaborative Video Object Segmentation by Foreground-Background Integration
https://github.com/PhanTom2003/PSPnet
https://github.com/PuchatekwSzortach/voc_fcn
https://github.com/Rintarooo/PSPNet
https://github.com/RituYadav92/Image-segmentation
https://github.com/Rosie-Brigham/sesmeg
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.