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/sthalles/PyTorch-BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/sthalles/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/stone96123/rle
Dynamic Prototype Mask for Occluded Person Re-Identification
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunjesse/SimCLR_pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunnyahlawat1713/face-mask-detector-python
https://github.com/sunnysoni97/single_image_fl
https://github.com/swasun/VQ-VAE-images
https://github.com/swetak20/Deep_into_CNNs
https://github.com/swxhss/densenet_tensorflow
https://github.com/sxzrt/semi-structured-dataset-representations
https://github.com/syfafterzy/PCB_RPP
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/syfafterzy/pcb_rpp_for_reid
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
https://github.com/sylviesss/pytorch-simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/szq0214/S2-BNN
https://github.com/t0930198/backup
https://github.com/ta9ryuWalrus/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/tachitachi/GradientReversal
https://github.com/taem98/deep_sort_face
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/talipucar/PyFlow_BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/talipucar/PyFlow_SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/tamnil/esp32-xtensa-study
https://github.com/tanjeffreyz/deep-residual-learning
https://github.com/tantara/JejuNet
https://github.com/tao-tao-tao-tao-tao/diffusion_suction
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/taroogura/Person_reID_baseline_pytorch
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/taylor-914/DenseNet
https://github.com/tbmoon/facenet
https://github.com/tcheung99/ResNet_MiniProject
https://github.com/technioncomputationalmrilab/colorectal_cancer_ai
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.