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Affordance Detection
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Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation.
Image source: Object-Based Affordances Detection with Convolutional Neural Networks and Dense Conditional Random Fields
Unlike other visual or physical properties that mainly describe the object alone, affordances indicate functional interactions of object parts with humans.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Gorilla-Lab-SCUT/AffordanceNet
공식 코드3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
https://github.com/gipfelen/dtcg-net
공식 코드https://github.com/lhc1224/OSAD_Net
공식 코드https://github.com/lmur98/epic_kitchens_affordances
공식 코드https://github.com/lulsheng/cbce-net
공식 코드Phrase-Based Affordance Detection via Cyclic Bilateral Interaction
https://github.com/nqanh/affordance-net
공식 코드AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
https://github.com/philipposg/osdd
공식 코드Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study
https://github.com/zhihou7/HOI-CL
공식 코드Affordance Transfer Learning for Human-Object Interaction Detection
https://github.com/zhihou7/HOI-CL-OneStage
공식 코드Affordance Transfer Learning for Human-Object Interaction Detection
https://github.com/beapc18/AffordanceNet
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
https://github.com/eduard626/interaction-tensor-affordances
https://github.com/lhc1224/cross-view-affordance-grounding
Phrase-Based Affordance Detection via Cyclic Bilateral Interaction
https://github.com/lhc1224/cross-view-ag
Phrase-Based Affordance Detection via Cyclic Bilateral Interaction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.