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/kakaobrain/scrl
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/kamanphoebe/motiondetection
https://github.com/kaseris/ILSVRCPlus
https://github.com/kaustavsenkvs/DeepSort-Algorithm
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/kawshik8/DL-project
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/kclip/localized-adaptive-risk-control
https://github.com/keetsky/Net_ghostVLAD-pytorch
https://github.com/kekeller/semantic_soy_deeplabv3plus
https://github.com/keras-team/keras-applications
https://github.com/kevinmusgrave/pytorch-adapt
https://github.com/khrlimam/facenet
https://github.com/kikacaty/adv_guide
https://github.com/kilsenp/triplet-reid-pytorch
https://github.com/kingcong/densenet
https://github.com/kingcong/gpu_FaceAttribute
https://github.com/kingcong/gpu_SE-Net
https://github.com/kingcong/mobilenetv2
https://github.com/kingcong/models/tree/main/MGN
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
https://github.com/kingcong/resnet
https://github.com/kiranu1024/deep-learning
https://github.com/kishanAk21/Skin-Cancer-Image-Classification
https://github.com/kishorkuttan/traffic_net
https://github.com/km1414/CNN-models
https://github.com/kmzzhang/nbi
https://github.com/knorth55/chainer-sort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/konstantinos-p/image_classification_SOTA
https://github.com/koshian2/ResNet-MultipleFramework
https://github.com/krafton-ai/mini-batch-cl
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/krishnakarthi/COVID-19_Prediction
https://github.com/kshitij1489/object-tracking
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/kuanzi/Yolov3_DeepSort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.