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/nabarunbaruaAIML/ATCC_Yolov5
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/nadee13/deep_sort_project
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/nanzhaogang/contrib/tree/master/application/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/nashawnch/Computer-Vision-FerrariDetective
https://github.com/nashawnch/ComputerVision-FerrariDetective
https://github.com/ndb796/pytorch-adversarial-training-cifar
https://github.com/neuleaf/MobileNetV2
https://github.com/nickgkan/beauty_detr
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/nickgkan/butd_detr
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/nicolalandro/Padam
https://github.com/ninatu/mood_challenge
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/niranjana98/Image-Classification
https://github.com/nithinksath96/Face-Classification-and-Verification-Using-Convolutional-Neural-Networks
https://github.com/noahfl/densenet-sdr
https://github.com/nolanliou/PeopleSegmentationDemo
https://github.com/northeastsquare/effficientnet
https://github.com/nsom/Basic-Models
https://github.com/nsom/VGG16
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nzw0301/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/oaqoe-DWQ/Yolov5_DeepSort_Pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/okason97/DenseNet-Tensorflow2
https://github.com/okason97/handshape-recognition
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/open-air-sun/pq-transformer
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/open-mmlab/OpenUnReID
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmselfsup
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/open-mmlab/mmtracking
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/openvinotoolkit/deep-object-reid
https://github.com/optimization-ai/sogclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/oskyhn/CNNs-Without-Borders
https://github.com/osmr/imgclsmob
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.