https://github.com/IvyYZ/POAR
공식 코드POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Papers with Code 아카이브
Pedestrian Attribute Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Pedestrian attribution recognition is the task of recognizing pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.
<span style="color:grey; opacity: 0.6">( Image credit: HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/SUTDCV/UAV-Human
공식 코드UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
https://github.com/Shuyu-XJTU/APTM
공식 코드https://github.com/chufengt/iccv19_attribute
공식 코드https://github.com/event-ahu/openpar
공식 코드SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm
https://github.com/event-ahu/vtf_par
공식 코드Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition
https://github.com/guotengg/sspnet
공식 코드https://github.com/junkunyuan/hap
공식 코드HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
https://github.com/li-wanhua/label2label
공식 코드Label2Label: A Language Modeling Framework for Multi-Attribute Learning
https://github.com/liuzhuang13/DenseNet
공식 코드https://github.com/mlnjeongpark/vita-par
공식 코드https://github.com/opengvlab/hulk
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/humanbench
공식 코드HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
https://github.com/opengvlab/unihcp
공식 코드https://github.com/sdret/a-solution-to-co-occurence-bias-in-pedestrian-attribute-recognition
공식 코드https://github.com/sdret/pedestrian-attribute-recognition-as-label-balanced-multi-label-learning
공식 코드Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
https://github.com/tinyvision/SOLIDER
공식 코드https://github.com/valencebond/Rethinking_of_PAR
공식 코드https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
공식 코드https://github.com/xh-liu/HydraPlus-Net
공식 코드HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
https://github.com/xwf199/parformer
공식 코드PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition
https://github.com/zplusdragon/plip
공식 코드PLIP: Language-Image Pre-training for Person Representation Learning
https://github.com/0492wzl/tensorflow_slim_densenet
https://github.com/0jason000/DenseNet
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-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
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-1/ms-code-22
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.