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Human-object interaction (HOI) detection as a downstream of object detectiontasks requires localizing pairs of humans and objects and extracting thesemantic relationships between humans and objects from an image. Recently,one-stage approaches have become a new trend for this task due to their highefficiency. However, these approaches focus on detecting possible interactionpoints or filtering human-object pairs, ignoring the variability in thelocation and size of different objects at spatial scales. To address thisproblem, we propose a transformer-based method, QAHOI (Query-Based Anchors forHuman-Object Interaction detection), which leverages a multi-scale architectureto extract features from different spatial scales and uses query-based anchorsto predict all the elements of an HOI instance. We further investigate that apowerful backbone significantly increases accuracy for QAHOI, and QAHOI with atransformer-based backbone outperforms recent state-of-the-art methods by largemargins on the HICO-DET benchmark. The source code is available at$\href{https://github.com/cjw2021/QAHOI}{\text{this https URL}}$.

QAHOI: Query-Based Anchors for Human-Object Interaction Detection