Affordance Transfer Learning for Human-Object Interaction Detection

Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel objects and recognize affordances. Specifically, HOI representations can be decoupled into a combination of affordance and object representations, making it possible to compose novel interactions by combining affordance representations and novel object representations from additional images, i.e. transferring the affordance to novel objects. With the proposed affordance transfer learning, the model is also capable of inferring the affordances of novel objects from known affordance representations. The proposed method can thus be used to 1) improve the performance of HOI detection, especially for the HOIs with unseen objects; and 2) infer the affordances of novel objects. Experimental results on two datasets, HICO-DET and HOI-COCO (from V-COCO), demonstrate significant improvements over recent state-of-the-art methods for HOI detection and object affordance detection. Code is available at

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Human-Object Interaction Concept Discovery HICO-DET Affordance Transfer Unknown (AP) 24.38 # 3
Affordance Recognition HICO-DET ATL HICO 59.44 # 2
COCO-Val2017 52.01 # 2
Object365 50.94 # 2
Novel classes 15.64 # 2
Affordance Recognition HICO-DET(Unknown Concepts) ATL COCO-Val2017 36.80 # 2
Obj365 34.38 # 2
HICO 42.00 # 2
Novel Classes 15.64 # 2


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