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Human-object interaction (HOI) detection aims to extract interactinghuman-object pairs and their interaction categories from a given natural image.Even though the labeling effort required for building HOI detection datasets isinherently more extensive than for many other computer vision tasks,weakly-supervised directions in this area have not been sufficiently exploreddue to the difficulty of learning human-object interactions with weaksupervision, rooted in the combinatorial nature of interactions over the objectand predicate space. In this paper, we tackle HOI detection with the weakestsupervision setting in the literature, using only image-level interactionlabels, with the help of a pretrained vision-language model (VLM) and a largelanguage model (LLM). We first propose an approach to prune non-interactinghuman and object proposals to increase the quality of positive pairs within thebag, exploiting the grounding capability of the vision-language model. Second,we use a large language model to query which interactions are possible betweena human and a given object category, in order to force the model not to putemphasis on unlikely interactions. Lastly, we use an auxiliaryweakly-supervised preposition prediction task to make our model explicitlyreason about space. Extensive experiments and ablations show that all of ourcontributions increase HOI detection performance.

Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors