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Human-Object Interaction (HOI) detection is an essential task to understandhuman-centric images from a fine-grained perspective. Although end-to-end HOIdetection models thrive, their paradigm of parallel human/object detection andverb class prediction loses two-stage methods' merit: object-guided hierarchy.The object in one HOI triplet gives direct clues to the verb to be predicted.In this paper, we aim to boost end-to-end models with object-guided statisticalpriors. Specifically, We propose to utilize a Verb Semantic Model (VSM) and usesemantic aggregation to profit from this object-guided hierarchy. Similarity KL(SKL) loss is proposed to optimize VSM to align with the HOI dataset's priors.To overcome the static semantic embedding problem, we propose to generatecross-modality-aware visual and semantic features by Cross-Modal Calibration(CMC). The above modules combined composes Object-guided Cross-modalCalibration Network (OCN). Experiments conducted on two popular HOI detectionbenchmarks demonstrate the significance of incorporating the statistical priorknowledge and produce state-of-the-art performances. More detailed analysisindicates proposed modules serve as a stronger verb predictor and a moresuperior method of utilizing prior knowledge. The codes are available at\url{https://github.com/JacobYuan7/OCN-HOI-Benchmark}.

Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics