https://github.com/MindSpore-scientific/code-5/tree/main/Spatial%20and%20Temporal%20Mutual%20Promotion%20for%20Video-based%20Person%20Re-identification
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
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/MioChiu/MobileNet_V2_TensorFlow
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/Mnpr/OC-DeepLearning
https://github.com/Mnpr/Thoractic-Diseases-Detection
https://github.com/Mnpr/Thoractic_Disease_Detection
https://github.com/Moncef-Bj/GAF-Net-for-Video-Based-Person-Re-Identification
https://github.com/MrD1360/Keras-implementation-of-MobileNetv3-with-L-RASSP
https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
https://github.com/NASA-NeMO-Net/NeMO-Net
https://github.com/NIRVANALAN/magnifiernet_reid
A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification
https://github.com/NIRVANALAN/reid_baseline
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
https://github.com/NVIDIA/retinanet-examples
https://github.com/NVIDIAAICITYCHALLENGE/2020AICITY_Code_From_Top_Teams
Simulating Content Consistent Vehicle Datasets with Attribute Descent
https://github.com/NVlabs/DG-Net
https://github.com/Nadavc220/DomainAdversarialTrainingOfNeuralNetworks
https://github.com/Namkyeong/BGRL_Pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/Neverland610/MSUDA_REID
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
https://github.com/Nguyendat-bit/MobilenetV2
https://github.com/OML-Team/open-metric-learning
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/PPPrior/i3d-pytorch
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_BYOL_model.md
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/algorithm_introduction/ReID.md
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleOCR
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/Pangoraw/GeoSSL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/PaperCodeReview/MoCo-TF
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.