  The interaction decoder utilized in prevalent Transformer-based HOI detectors
typically accepts pre-composed human-object pairs as inputs. Though achieving
remarkable performance, such paradigm lacks feasibility and cannot explore
novel combinations over entities during decoding. We present L OGIC HOI, a new
HOI detector that leverages neural-logic reasoning and Transformer to infer
feasible interactions between entities. Specifically, we modify the
self-attention mechanism in vanilla Transformer, enabling it to reason over the
<human, action, object> triplet and constitute novel interactions. Meanwhile,
such reasoning process is guided by two crucial properties for understanding
HOI: affordances (the potential actions an object can facilitate) and proxemics
(the spatial relations between humans and objects). We formulate these two
properties in first-order logic and ground them into continuous space to
constrain the learning process of our approach, leading to improved performance
and zero-shot generalization capabilities. We evaluate L OGIC HOI on V-COCO and
HICO-DET under both normal and zero-shot setups, achieving significant
improvements over existing methods.
