Papers › Detecting Human-Object Interactions with Action Co-occurrence Priors

Detecting Human-Object Interactions with Action Co-occurrence Priors

1 Aug 2020ECCV 2020 8archive 2025-07-28

Dong-Jin Kim Xiao Sun Jinsoo Choi Stephen Lin In So Kweon

A common problem in human-object interaction (HOI) detection task is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution. The lack of positive labels can lead to low classification accuracy for these classes. Towards addressing this issue, we observe that there exist natural correlations and anti-correlations among human-object interactions. In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially in rare classes. The utility of our approach is demonstrated experimentally, where the performance of our approach exceeds the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO.

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