Title: Zero-shot Human-Object Interaction Recognition by Bridging Generative and Contrastive Image-Language Models. Abstract: Existing studies in Human-Object Interaction (HOI) recognition rely heavily on costly human-annotated labels, limiting the application of HOI in real-world scenarios like retail and surveillance. To address this issue, this paper investigates a new zero-shot setup where no HOI labels are available for any image. We propose a novel heterogenous teacher-student framework that bridges two types of pre-trained models, namely contrastive (e.g., CLIP) and generative (e.g., GIT) image-language models. To bridge their gap, we introduce pseudo-label distillation to extract HOI probabilities from image captions to train the student classifier. Our method leverages the complementary strengths of both models. As a result, the student model has "the best of two worlds", e.g., the compact backbone of a contrastive model and the fine-grained discriminability of a generative (captioning) model. It achieves 49.6 mAP on the HICO dataset without any ground-truth labels, becoming a new state-of-the-art that outperforms previous supervised approaches. Code will be released upon acceptance.