https://github.com/zhihou7/scl
공식 코드Discovering Human-Object Interaction Concepts via Self-Compositional Learning
Papers with Code 아카이브
Human-Object Interaction Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 6
- 보존된 평가 기록
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Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i.e., humans) and target (i.e., objects) of interaction, and ii) the classification of the interaction labels.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhumanli/geohoi
공식 코드Geometric Features Enhanced Human-Object Interaction Detection
https://github.com/zhumanli/sgcn4hoi
공식 코드A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection
https://github.com/zitiantang/thermal-im
공식 코드What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging
https://github.com/zyong812/stip
공식 코드https://github.com/2023-MindSpore-1/ms-code-5/tree/main/hake
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/BigRedT/no_frills_hoi_det
https://github.com/CarstenEpic/humos
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild
https://github.com/ChicyChen/my_d3d
https://github.com/Imposingapple/Transferable_Interactiveness_Network_with_Partpair
Pairwise Body-Part Attention for Recognizing Human-Object Interactions
https://github.com/IreneMahhy/no_frills_hoi_det-mod
https://github.com/IreneMahhy/no_frills_hoi_det-mod-master
https://github.com/JamesZhutheThird/Rb-PaStaNet
Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States
https://github.com/Mind23-2/MindCode-141
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
https://github.com/MindSpore-paper-code-2/code3/tree/main/trn
https://github.com/MindSpore-paper-code-3/code9/tree/main/hake
https://github.com/MindSpore-paper-code-3/code9/tree/main/luke
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
https://github.com/Monash-CyPhi-AI-Research-Lab/ernet
https://github.com/MuchHair/PD-Net-Extended-Version
Polysemy Deciphering Network for Robust Human-Object Interaction Detection
https://github.com/TaiwanRobert/iCAN_for_live_video
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
https://github.com/Tushar-N/interaction-hotspots
https://github.com/VivekUdupa/CPSC8810-DeepLearning_vkoodli_shonnah
https://github.com/beapc18/AffordanceNet
Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks
https://github.com/facebookresearch/detectron
https://github.com/fpv-iplab/stillfast
StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/lahaproject/inavit
Interaction Region Visual Transformer for Egocentric Action Anticipation
https://github.com/lalithjets/domain-adaptation-in-mtl
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection
https://github.com/lalithjets/domain-generalization-for-surgical-scene-graph
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection
https://github.com/lalithjets/global-reasoned-multi-task-model
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection
https://github.com/metalbubble/TRN-pytorch
https://github.com/okankop/MFF-pytorch
https://github.com/rohitgirdhar/AttentionalPoolingAction
https://github.com/weijianan1/logichoi
https://github.com/wyhuai/SkillMimic
SkillMimic: Learning Basketball Interaction Skills from Demonstrations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.