<p>Human–object interaction detection (HOID) refers to localizing interactive human–object pairs in images and identifying the interactions. Since there could be an exponential number of object–action combinations, labeled data is limited—leading to a long-tail distribution problem. Recently, zero-shot learning emerged as a solution, with end-to-end transformer-based object detectors adapted for HOID becoming successful frameworks. However, their primary focus is designing improved decoders for learning entangled or disentangled interpretations of interactions. We advocate that HOI-specific cues must be anticipated at the encoder stage itself to obtain a stronger scene interpretation. Consequently, we build a top-down framework named Funnel-HOI inspired by the human tendency to grasp well-defined concepts first and then associate them with abstract concepts during scene understanding. Specifically, we first probe an image for the presence of objects (well-defined concepts) and then probe for actions (abstract concepts) associated with them. A novel asymmetric co-attention mechanism mines these cues utilizing multimodal information (incorporating zero-shot capabilities) and yields stronger interaction representations at the encoder level. Furthermore, a novel loss is devised that considers object–action relatedness and regulates the misclassification penalty better than existing loss functions for guiding the interaction classifier. Extensive experiments on the HICO-DET and V-COCO datasets across fully-supervised and six zero-shot settings reveal our state-of-the-art performance, with up to 12.4 and 4.5% gains for unseen and rare HOI categories, respectively.</p>