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/zhiyinshao-h/lgur
공식 코드Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
https://github.com/zhiyinshao-h/unipt
공식 코드Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification
https://github.com/zhoujiahuan1991/aaai2025-lreid-dask
공식 코드https://github.com/zhunzhong07/CamStyle
공식 코드https://github.com/zhunzhong07/ECN
공식 코드Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
https://github.com/zhunzhong07/Random-Erasing
공식 코드https://github.com/zifyloo/SSAN
공식 코드Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification
https://github.com/zkcys001/UDAStrongBaseline
공식 코드Group-aware Label Transfer for Domain Adaptive Person Re-identification
https://github.com/zkcys001/cfd
공식 코드Calibrated Feature Decomposition for Generalizable Person Re-Identification
https://github.com/zplusdragon/cion_reidzoo
공식 코드Cross-video Identity Correlating for Person Re-identification Pre-training
https://github.com/zplusdragon/plip
공식 코드PLIP: Language-Image Pre-training for Person Representation Learning
https://github.com/zplusdragon/reid5o_orbench
공식 코드ReID5o: Achieving Omni Multi-modal Person Re-identification in a Single Model
https://github.com/zydou/Deep-Person
공식 코드Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
https://github.com/zyk100/llcm
공식 코드https://github.com/zzhsysu/VA-ReID
공식 코드Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
https://gitee.com/gai-shaoyan/mind3d
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/0492wzl/tensorflow_slim_densenet
https://github.com/0jason000/DenseNet
https://github.com/0jason000/MobileNet_V2
https://github.com/0jason000/MobilleNetV3
https://github.com/0jason000/ShuffleNetV1
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/1170500804/MoCo
https://github.com/2020-sk49/VFOST
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/2020132075/conmae
https://github.com/2023-MindSpore-1/ms-code-119
Exploring Visual Context for Weakly Supervised Person Search
https://github.com/2023-MindSpore-1/ms-code-120
https://github.com/2023-MindSpore-1/ms-code-14
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-1/ms-code-146
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/3D_DenseNet
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/AlignedReID
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/AlignedReID%2B%2B
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
https://github.com/2023-MindSpore-1/ms-code-177
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-208
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.