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We propose a novel one-stage Transformer-based semantic and spatial refinedtransformer (SSRT) to solve the Human-Object Interaction detection task, whichrequires to localize humans and objects, and predicts their interactions.Differently from previous Transformer-based HOI approaches, which mostly focusat improving the design of the decoder outputs for the final detection, SSRTintroduces two new modules to help select the most relevant object-action pairswithin an image and refine the queries' representation using rich semantic andspatial features. These enhancements lead to state-of-the-art results on thetwo most popular HOI benchmarks: V-COCO and HICO-DET.

What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions