computer-vision-11

Affordance Recognition

3260 papers • 126 benchmarks • 313 datasets

Affordance recognition from Human-Object Interaction

(Image credit: Papersgraph)

Benchmarks

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HICO-DET

HICO-DET(Unknown Concepts)

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i

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HICO-DET

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Most implemented papers

Visual Compositional Learning for Human-Object Interaction Detection

Y. Qiao, D. Tao, Zhi Hou, Xiaojiang Peng•Thu Jul 23 2020

A deep Visual Compositional Learning (VCL) framework is devised, which is a simple yet efficient framework to effectively address the problem of human-Object interaction detection and largely alleviates the long-tail distribution problem and benefits low-shot or zero-shot HOI detection.

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Affordance Transfer Learning for Human-Object Interaction Detection

Y. Qiao, D. Tao, Baosheng Yu, Zhi Hou, Xiaojiang Peng•Tue Apr 06 2021

An affordance transfer learning approach is introduced to jointly detect HOIs with novel object and recognize affordances, and is capable of inferring the affordances of novel objects from known affordance representations.

135 0

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Discovering Human-Object Interaction Concepts via Self-Compositional Learning

D. Tao, Baosheng Yu, Zhi Hou•Sat Mar 26 2022

A novel and challenging task for a comprehensive HOI understanding, which is termed as HOI Concept Discovery, and a self-compositional learning framework (or SCL) for HOI concept discovery is introduced, which enables the learning on both known and unknown HOI concepts.

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Detecting Human-Object Interaction via Fabricated Compositional Learning

Y. Qiao, D. Tao, B. Yu, Zhi Hou, Xiaojiang Peng•Sun Mar 14 2021

A novel HOI compositional learning framework is devised, termed as Fabricated Compositional Learning (FCL), to address the problem of open long-tailed HOI detection, which introduces an object fabricator to generate effective object representations, and then combines verbs and fabricated objects to compose new HOI samples.

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Fine-grained Affordance Annotation for Egocentric Hand-Object Interaction Videos

Ryosuke Furuta, Takuma Yagi, Zecheng Yu, Yifei Huang, Yusuke Goutsu, Y. Sato•Sat Dec 31 2022

An efficient annotation scheme to address issues of affordance in existing datasets is proposed by combining goal-irrelevant motor actions and grasp types as affordance labels and introducing the concept of mechanical action to represent the action possibilities between two objects.

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