https://github.com/SamvitJ/Accel
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
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/Scalsol/mega.pytorch
https://github.com/SilvioGiancola/SoccerNetv2-DevKit
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/StanLei52/GEBD
Generic Event Boundary Detection: A Benchmark for Event Segmentation
https://github.com/T-a-g-z/Yotube-VOS-OVS
https://github.com/TejasBajania/Mtech_pro
https://github.com/TejasBajania/Mtech_thesis_project
https://github.com/TripleJoy/SAM2MOT
https://github.com/UBC-CIC/COVID19-L3-Net
https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION
Generic Event Boundary Detection: A Benchmark for Event Segmentation
https://github.com/VisualComputingInstitute/mots_trackingonly_tools
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
https://github.com/Whiffe/mmaction2_YF
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
https://github.com/YeLyuUT/UAVidToolKit
https://github.com/YininKorea/Contour-aware-equipotential-learning
https://github.com/ZFTurbo/segmentation_models_3D
https://github.com/acaelles97/devis
https://github.com/achalddave/segment-any-moving
https://github.com/ahasan-haque/TMO-RAFT
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
https://github.com/aimagelab/rmsnet_soccer
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/aimerykong/predictive-filter-flow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
https://github.com/alibaba/EasyCV
https://github.com/ankhoreva/LucidDataDreaming
https://github.com/bestaar/predictiveFilterFlow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
https://github.com/birdman9390/MetaMaskTrack
https://github.com/branislavhesko/segmentation_framework
https://github.com/burakalperen/Pytorch-Semantic-Segmentation
https://github.com/caolele/caolele.github.io
Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning
https://github.com/cciprianmihai/Self_Driving_Car_NanoDegree_P2_AdvancedLaneLines
https://github.com/charlespikachu/mcibi
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation
https://github.com/cioppaanthony/rt-sbs
https://github.com/cj-mclaughlin/segmentation_research
https://github.com/code-implementation1/Code6/tree/main/PSPNet
https://github.com/cooparation/FCN_play
https://github.com/cplab/annolid
https://github.com/cufctl/segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.