Discovering Human Interactions With Novel Objects via Zero-Shot Learning

We aim to detect human interactions with novel objects through zero-shot learning. Different from previous works, we allow unseen object categories by using its semantic word embedding. To do so, we design a human-object region proposal network specifically for the human-object interaction detection task. The core idea is to leverage human visual clues to localize objects which are interacting with humans. We show that our proposed model can outperform existing methods on detecting interacting objects, and generalize well to novel objects. To recognize objects from unseen categories, we devise a zero-shot classification module upon the classifier of seen categories. It utilizes the classifier logits for seen categories to estimate a vector in the semantic space, and then performs nearest search to find the closest unseen category. We validate our method on V-COCO and HICO-DET datasets, and obtain superior results on detecting human interactions with both seen and unseen objects.