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Human Object Interaction (HOI) detection aims to localize and infer therelationships between a human and an object. Arguably, training supervisedmodels for this task from scratch presents challenges due to the performancedrop over rare classes and the high computational cost and time required tohandle long-tailed distributions of HOIs in complex HOI scenes in realisticsettings. This observation motivates us to design an HOI detector that can betrained even with long-tailed labeled data and can leverage existing knowledgefrom pre-trained models. Inspired by the powerful generalization ability of thelarge Vision-Language Models (VLM) on classification and retrieval tasks, wepropose an efficient Adaptive HOI Detector with Concept-guided Memory (ADA-CM).ADA-CM has two operating modes. The first mode makes it tunable withoutlearning new parameters in a training-free paradigm. Its second modeincorporates an instance-aware adapter mechanism that can further efficientlyboost performance if updating a lightweight set of parameters can be afforded.Our proposed method achieves competitive results with state-of-the-art on theHICO-DET and V-COCO datasets with much less training time. Code can be found athttps://github.com/ltttpku/ADA-CM.

Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory