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In this paper, we investigate the task of zero-shot human-object interaction(HOI) detection, a novel paradigm for identifying HOIs without the need fortask-specific annotations. To address this challenging task, we employ CLIP, alarge-scale pre-trained vision-language model (VLM), for knowledge distillationon multiple levels. Specifically, we design a multi-branch neural network thatleverages CLIP for learning HOI representations at various levels, includingglobal images, local union regions encompassing human-object pairs, andindividual instances of humans or objects. To train our model, CLIP is utilizedto generate HOI scores for both global images and local union regions thatserve as supervision signals. The extensive experiments demonstrate theeffectiveness of our novel multi-level CLIP knowledge integration strategy.Notably, the model achieves strong performance, which is even comparable withsome fully-supervised and weakly-supervised methods on the public HICO-DETbenchmark.

Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels