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/CryptoSalamander/pytorch_paper_implementation
https://github.com/D-X-Y/GDAS
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
https://github.com/D-X-Y/NAS-Projects
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
https://github.com/D-X-Y/ResNeXt-DenseNet
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/DaikiTanak/manifold_mixup
https://github.com/Daniel-H-99/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Darth-Kronos/Unsupervised-Domain-Adaptation
https://github.com/Deci-AI/super-gradients
https://github.com/DeepMark/deepmark
https://github.com/DeepTrial/Retina-VesselNet
https://github.com/Delong-liu-bupt/Composed_Person_Retrieval
Person Re-identification by Contour Sketch under Moderate Clothing Change
https://github.com/Delong-liu-bupt/Word4Per
Person Re-identification by Contour Sketch under Moderate Clothing Change
https://github.com/Demonhesusheng/Reid
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/DuanYiqun/Auto-ReID-Fast
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
https://github.com/Duplums/bhb10k-dl-benchmark
https://github.com/EchoItLiu/SelfGait
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
https://github.com/EdwinAlegria/object_semantic_deeplabv3
https://github.com/Elvictor01/Plant-Pathology-2020---FGVC7-Identify-the-category-of-foliar-diseases-in-apple-trees
https://github.com/EricAlcaide/MiniFold
https://github.com/EstherBear/implementation-of-pruning-filters
https://github.com/Evgeneus/MoCo
https://github.com/FEIfei-coder/circle-loss-for-reid
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/Faceplugin-ltd/FaceRecognition-Android
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/FaizaAslam2424/Faiza-Aslam
Batch DropBlock Network for Person Re-identification and Beyond
https://github.com/FlowWind1999/pointnet-2
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/FlyingRoastDuck/PGPPM
Leveraging Virtual and Real Person for Unsupervised Person Re-identification
https://github.com/FrancescoSaverioZuppichini/ResNet
https://github.com/GJTNB/reading-memo
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
https://github.com/GKalliatakis/Keras-VGG16-places365
https://github.com/GNAYUOHZ/ReID-MGN
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
https://github.com/GSNCodes/Breast_Cancer_Detection_CNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.