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

arXiv: https://arxiv.org/abs/2109.04047 · HTML: https://mlsanity.com/paper/2109.04047/

## 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 (most cited first, 30 of 56)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Exploring Visual Relationship for Image Captioning](https://mlsanity.com/paper/1809.07041/) (2018)
- [Detecting and Recognizing Human-Object Interactions](https://mlsanity.com/paper/1704.07333/) (2017)
- [Visual Semantic Role Labeling](https://mlsanity.com/paper/1505.04474/) (2015)
- [Learning Human-Object Interactions by Graph Parsing Neural Networks](https://mlsanity.com/paper/1808.07962/) (2018)
- [Scene Graph Generation from Objects, Phrases and Region Captions](https://mlsanity.com/paper/1707.09700/) (2017)
- [PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection](https://mlsanity.com/paper/1912.12898/) (2019)
- [Harnessing Deep Neural Networks with Logic Rules](https://mlsanity.com/paper/1603.06318/) (2016)
- [Visual Compositional Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2007.12407/) (2020)
- [iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection](https://mlsanity.com/paper/1808.10437/) (2018)
- [DRG: Dual Relation Graph for Human-Object Interaction Detection](https://mlsanity.com/paper/2008.11714/) (2020)
- [VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions](https://mlsanity.com/paper/2003.05541/) (2020)
- [Visual Translation Embedding Network for Visual Relation Detection](https://mlsanity.com/paper/1702.08319/) (2017)
- [Learning Human-Object Interaction Detection using Interaction Points](https://mlsanity.com/paper/2003.14023/) (2020)
- [Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning](https://mlsanity.com/paper/2007.08844/) (2020)
- [Scene Graph Generation with External Knowledge and Image Reconstruction](https://mlsanity.com/paper/1904.00560/) (2019)

## Built on by (most cited first, 11 of 11)

- [Generative Bias for Robust Visual Question Answering](https://mlsanity.com/paper/2208.00690/) (2022)
- [Self-Sufficient Framework for Continuous Sign Language Recognition](https://mlsanity.com/paper/2303.11771/) (2023)
- [Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection](https://mlsanity.com/paper/2207.02400/) (2022)
- [Distance Matters in Human-Object Interaction Detection](https://mlsanity.com/paper/2207.01869/) (2022)
- [Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.00448/) (2022)
- [Single-Modal Entropy based Active Learning for Visual Question Answering](https://mlsanity.com/paper/2110.10906/) (2021)
- [Precision-Enhanced Human-Object Contact Detection via Depth-Aware Perspective Interaction and Object Texture Restoration](https://mlsanity.com/paper/2412.09920/) (2024)
- [Geometric Features Enhanced Human-Object Interaction Detection](https://mlsanity.com/paper/2406.18691/) (2024)
- [Human Action Co-occurrence in Lifestyle Vlogs using Graph Link Prediction](https://mlsanity.com/paper/2309.06219/) (2023)
- [FreeA: Human-object Interaction Detection using Free Annotation Labels](https://mlsanity.com/paper/2403.01840/) (2024)
- [Funnel-HOI: Top-Down Perception for Zero-Shot HOI Detection](https://mlsanity.com/paper/2507.12628/) (2025)

## 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.