Detecting Human-Object Interactions via Functional Generalization
This work addresses a key challenge in computer vision for applications like robotics and surveillance, offering incremental improvements with practical generalization benefits.
The paper tackles human-object interaction detection in images by leveraging functional similarity between objects, achieving a 2.5% mAP gain on HICO-Det and showing improved zero-shot generalization to unseen objects.
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.