mlsanityData to 14 Oct 2026

ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection

arXiv 2109.04047 ↗First posted 9 Sep 2021cs.CV cs.AIcited by 11

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Abstract (arXiv)

A common problem in the task of human-object interaction (HOI) detection 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 on rare classes. The efficacy of our approach is demonstrated experimentally, where the performance of our approach consistently improves over the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 56 of its 102 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

56 of 102 references.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 3; 2025: 1.

11 papers.