Papers with Code 아카이브
Person Re-Identification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 41
- 보존된 평가 기록
- 533
Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Sangkwun/Resnet
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/Seonghoon-Yu/MoCov2_Pytorch_tutorial
https://github.com/SethEBaldwin/Resnet
https://github.com/ShichengChen/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/ShiyuLiang/odin-pytorch
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/SikaStar/Dual-Refinement
https://github.com/SimJeg/FC-DenseNet
https://github.com/Simon4Yan/Learning-via-Translation
https://github.com/Stomper10/CheXpert
https://github.com/SuperbTUM/REID-IDM
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
https://github.com/SurajDonthi/Clean-ST-ReID-Multi-Target-Multi-Camera-Tracking
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/SurajDonthi/Multi-Camera-Person-Re-Identification
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/SurajDonthi/Multi-Target-Multi-Camera-Tracking-ST-ReID
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/SurajDonthi/Multi-Target-Multi-Camera-Tracking-ST-ReID-Clean-Code
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/Syarujianai/deeplab-commented
https://github.com/TRAILab/ST-SLidR
https://github.com/TTMRonald/MobileNet_Zoo
https://github.com/TallyH0/aihubpedkorean
https://github.com/ThanasisMattas/smartflow
https://github.com/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/TianmingQiu/HydraPlusNet
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
https://github.com/TianyiZhang0315/CV_FinalProject
https://github.com/TinyZeaMays/CircleLoss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/Traffic-X/Open-TransMind
https://github.com/TrinhQuocNguyen/CORE-ReID
https://github.com/UESBTC/DenseNet-Tensorflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.