# Detecting Human-Object Interactions via Functional Generalization

## TL;DR

This paper introduces a simple, efficient model for detecting human-object interactions in images, leveraging functional similarity among objects to improve accuracy and generalization, including zero-shot scenarios.

## Contribution

The paper proposes a novel approach that uses functional generalization to enhance HOI detection, achieving state-of-the-art results and better zero-shot performance.

## Key findings

- Over 2.5% mAP improvement on HICO-Det dataset
- Significant gains in zero-shot HOI detection
- Model generalizes to unseen objects using generic detectors

## Abstract

We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. The proposed model is simple and efficiently uses the data, visual features of the human, relative spatial orientation of the human and the object, and the knowledge that functionally similar objects take part in similar interactions with humans. We provide extensive experimental validation for our approach and demonstrate state-of-the-art results for HOI detection. On the HICO-Det dataset our method achieves a gain of over 2.5% absolute points in mean average precision (mAP) over state-of-the-art. We also show that our approach leads to significant performance gains for zero-shot HOI detection in the seen object setting. We further demonstrate that using a generic object detector, our model can generalize to interactions involving previously unseen objects.