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/hsfzxjy/dprn
https://github.com/hsinyilin19/resnetvae
https://github.com/htdt/self-supervised
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/htso/Pedestrian_in_few_shots
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch
https://github.com/huanghoujing/beyond-part-models
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
https://github.com/huanghoujing/person-reid-triplet-loss-baseline
https://github.com/hugh67/reid-MGN-pytorch
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
https://github.com/hukefei/deep-person-reid-master
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/sparseinst
https://github.com/huynhtuan17ti/FaceNet-OneShotLearning
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hycis/TensorGraph
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hz2538/iot_JennyGo
https://github.com/iArunava/ResNet
https://github.com/iN1k1/deep-pyramidal-representations-person-re-identification
https://github.com/iballester/SPiKE
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/idobronstein/vision_networks
https://github.com/ihamdi/Covid-xRay-Classification
https://github.com/ikh-innovation/roboweldar-votenet
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/ikhlestov/vision_networks
https://github.com/ikigai-aa/Face-Mask-Detector-using-MobileNetV2
https://github.com/imatif17/Joint-Unsupervised-Domain-adaption-and-Knowledge-distillation
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
https://github.com/imkoushik22/Walmart-Hackathon
https://github.com/immuno121/audio_source_classification
https://github.com/infinitylogesh/Kaggle-Quora-Incincere-question-classification
https://github.com/interestingzhuo/foodretreival
https://github.com/isakdiaz/PlantNet_MobileNetV2
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/ithuanhuan/EANet-master
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
https://github.com/itijyou/ademxapp
https://github.com/ivychill/reid
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.