Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels
This addresses the problem of detecting human-object interactions without task-specific annotations for computer vision applications, representing an incremental advance.
The paper tackles zero-shot human-object interaction detection by using CLIP for multi-level knowledge distillation, achieving performance comparable to some supervised methods on the HICO-DET benchmark.
In this paper, we investigate the task of zero-shot human-object interaction (HOI) detection, a novel paradigm for identifying HOIs without the need for task-specific annotations. To address this challenging task, we employ CLIP, a large-scale pre-trained vision-language model (VLM), for knowledge distillation on multiple levels. Specifically, we design a multi-branch neural network that leverages CLIP for learning HOI representations at various levels, including global images, local union regions encompassing human-object pairs, and individual instances of humans or objects. To train our model, CLIP is utilized to generate HOI scores for both global images and local union regions that serve as supervision signals. The extensive experiments demonstrate the effectiveness of our novel multi-level CLIP knowledge integration strategy. Notably, the model achieves strong performance, which is even comparable with some fully-supervised and weakly-supervised methods on the public HICO-DET benchmark.