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/Mind23-2/MindCode-168
https://github.com/Mind23-2/MindCode-3
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-5/tree/main/PAMTRI
https://github.com/Mind23-2/MindCode-5/tree/main/pcb
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/Mind23-2/MindCode-5/tree/main/pcb_rpp
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/Mind23-2/MindCode-67
https://github.com/Mind23-2/MindCode-70
https://github.com/Mind23-2/MindCode-76
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/MindCode-4/code-11/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindCode-4/code-14/tree/main/AGW
Deep Learning for Person Re-identification: A Survey and Outlook
https://github.com/MindCode-4/code-4/tree/main/rembert
https://github.com/MindCode-4/code-6/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindSpore-paper-code-2/code2/tree/main/PAMTRI
https://github.com/MindSpore-paper-code-2/code2/tree/main/osnet
https://github.com/MindSpore-paper-code-2/code2/tree/main/pcb
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/MindSpore-paper-code-2/code2/tree/main/pcb_rpp
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_mobilenetV2
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-2/code400/tree/main/MobileNet/mobilenetv2
https://github.com/MindSpore-paper-code-3/code2/tree/main/DDAG
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
https://github.com/MindSpore-paper-code-3/code2/tree/main/Deepsort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/MindSpore-paper-code-3/code2/tree/main/convnext
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code4/tree/main/frustum-pointnet
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code6/tree/main/Alexnet
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/MindSpore-paper-code-3/code6/tree/main/AlignedReID
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/DDAG
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code9/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/MindSpore-scientific-2/code-12/tree/main/Kronecker-Attention-Networks
End-to-End Deep Kronecker-Product Matching for Person Re-identification
https://github.com/MindSpore-scientific/code-14/tree/main/Spatial%20and%20Temporal%20Mutual%20Promotion%20for%20Video-based%20Person%20Re-identification
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
https://github.com/MindSpore-scientific/code-3/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.