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/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lannguyen0910/deep-efficient-person-reid
On the Unreasonable Effectiveness of Centroids in Image Retrieval
https://github.com/lannguyen0910/deep-efficient-reid
On the Unreasonable Effectiveness of Centroids in Image Retrieval
https://github.com/lavoiems/Cats-UDT
https://github.com/lavoiems/SADt
https://github.com/lavoiems/SPUDT
https://github.com/layumi/DG-Net
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/layumi/Person-reID-verification
A Discriminatively Learned CNN Embedding for Person Re-identification
https://github.com/layumi/Person_reID_baseline_pytorch
https://github.com/layumi/University1652-Baseline
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leftthomas/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/leonardovlibido/PersonRe-Identification
PersonNet: Person Re-identification with Deep Convolutional Neural Networks
https://github.com/letthedataconfess/Keras-triplet-loss-mnist
https://github.com/leule/DCDS
https://github.com/levan92/deep_sort_realtime
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/liangfu/mxnet-mobilenet-v2
https://github.com/libiseller/MobileNetV2-dynamicFPN
https://github.com/lightas/ICCV19_Pose_Guided_Occluded_Person_ReID
https://github.com/lightly-ai/lightly
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
https://github.com/linghu8812/tensorrt_tracker
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linspace100/AI_Traffic_Violation_Detector
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linspace100/AI_Traffic_Violence_Detector
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linspace100/AI_traffic_police
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linwenye/light-moco
https://github.com/lionelmessi6410/ntga
https://github.com/liuch37/image-processing
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/liuyao12/ConvNets-PDE-perspective
https://github.com/liuzhuang13/DenseNetCaffe
https://github.com/lixihan/Resnet
https://github.com/lixixibj/forecasting-with-time-series-imaging
https://github.com/lixixibj/tsImage-forecasting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.