# Reasoning About Human-Object Interactions Through Dual Attention Networks

arXiv: https://arxiv.org/abs/1909.04743 · HTML: https://mlsanity.com/paper/1909.04743/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [Forecasting Action through Contact Representations from First Person Video](https://mlsanity.com/paper/2102.00649/) (2021)
- [Human-Object Interaction Detection via Disentangled Transformer](https://mlsanity.com/paper/2204.09290/) (2022)
- [Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition](https://mlsanity.com/paper/2108.08633/) (2021)
- [Representing Videos as Discriminative Sub-graphs for Action Recognition](https://mlsanity.com/paper/2201.04027/) (2022)
- [Searching for Two-Stream Models in Multivariate Space for Video Recognition](https://mlsanity.com/paper/2108.12957/) (2021)
- [Free-Form Composition Networks for Egocentric Action Recognition](https://mlsanity.com/paper/2307.06527/) (2023)
- [ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video Recognition](https://mlsanity.com/paper/2110.15574/) (2021)
- [A Grammatical Compositional Model for Video Action Detection](https://mlsanity.com/paper/2310.02887/) (2023)

## Abstract (arXiv)

Objects are entities we act upon, where the functionality of an object is determined by how we interact with it. In this work we propose a Dual Attention Network model which reasons about human-object interactions. The dual-attentional framework weights the important features for objects and actions respectively. As a result, the recognition of objects and actions mutually benefit each other. The proposed model shows competitive classification performance on the human-object interaction dataset Something-Something. Besides, it can perform weak spatiotemporal localization and affordance segmentation, despite being trained only with video-level labels. The model not only finds when an action is happening and which object is being manipulated, but also identifies which part of the object is being interacted with. Project page: urlhttps://dual-attention-network.github.io/.