  Recently, the DETR framework has emerged as the dominant approach for
human--object interaction (HOI) research. In particular, two-stage
transformer-based HOI detectors are amongst the most performant and
training-efficient approaches. However, these often condition HOI
classification on object features that lack fine-grained contextual
information, eschewing pose and orientation information in favour of visual
cues about object identity and box extremities. This naturally hinders the
recognition of complex or ambiguous interactions. In this work, we study these
issues through visualisations and carefully designed experiments. Accordingly,
we investigate how best to re-introduce image features via cross-attention.
With an improved query design, extensive exploration of keys and values, and
box pair positional embeddings as spatial guidance, our model with enhanced
predicate visual context (PViC) outperforms state-of-the-art methods on the
HICO-DET and V-COCO benchmarks, while maintaining low training cost.
