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/robml/Deep-Learning-SSL-MoCo-SP21
https://github.com/roma-vinn/Object-tracking-lecture
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/roydenwa/cell-centroid-former
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/rpryzant/proxy-a-distance
https://github.com/rtanno21609/AdaptiveNeuralTrees
https://github.com/rusty1s/pytorch_cluster
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/rwightman/pytorch-image-models
https://github.com/ry/tensorflow-resnet
https://github.com/rybread1/lego-minifigure-classification
https://github.com/sabadijou/byol_multi_gpu
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/sagar-barapatre/Image-Classifier
https://github.com/saikrishnadas/ResNet-Pytorch
https://github.com/salarim/Semi-Supervised-CL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/saloni1998/Cifar-10
https://github.com/samirgholipour/speed_estimation
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/sammyamajumdar/ResNet50
https://github.com/sangHa0411/CIFAR100
https://github.com/sangHa0411/ImageNet
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sangdon/pac-ps-w
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sanoyo/dl-model
https://github.com/satishjasthi/Densenet_smplified
https://github.com/saunack/MobileNetv2-SSD
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
https://github.com/sayakpaul/SimCLR-in-TensorFlow-2
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/schizop/SA
Parameter-Free Spatial Attention Network for Person Re-Identification
https://github.com/scpark20/universal-music-translation
https://github.com/seansoleyman/cifar10-resnet
https://github.com/seasonyc/densenet
https://github.com/seathiefwang/MGN-pytorch
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN
https://github.com/sebsquire/FlowersRecognition_keras
https://github.com/seishinkikuchi/test
https://github.com/sek788432/Microsoft-Cats-and-Dogs-Image-Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.