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/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code8/tree/main/AlignedReID
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code9/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv2
https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet2
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/2024-MindSpore-1/Code4/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2024-MindSpore-1/Code5/tree/main/AlignedReID
Domain adaptation for person re-identification on new unlabeled data using AlignedReID++
https://github.com/2024-MindSpore-1/Code5/tree/main/AlignedReID%2B%2B
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/2024-MindSpore-1/Code6/tree/main/3D_DenseNet
https://github.com/2024-MindSpore-1/Code6/tree/main/AlignedReID
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/2manslkh/korean-food-classification-flask
https://github.com/3P2S/arcface
https://github.com/63days/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AD2605/Action-Recognition
https://github.com/AI-NERC-NUPT/DDB
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/AI-NERC-NUPT/PFH-OSNet
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/AKASH2907/Forgery-Classification-via-Domain-Adaptation
https://github.com/AdrianUng/keras-triplet-loss-mnist
https://github.com/Ahsanr312/Object-Detection-and-Tracking-using-YOLOv3-and-DeepSort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/AidenDurrant/MoCo-Pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AirBernard/DenseNet-by-Pytorch
https://github.com/Amisha-here/Image-Classifier
https://github.com/AnandS09/dip-deepsort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/AndlollipopFU/PCB
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/AndreiMoraru123/Hydra
https://github.com/Andrew-Qibin/ssdlite-pytorch
https://github.com/AndrewAtanov/simclr-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AndrewTal/MoCo-Pytorch
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/Anil1331/DenseNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.