  Human-Object Interaction Detection tackles the problem of joint localization
and classification of human object interactions. Existing HOI transformers
either adopt a single decoder for triplet prediction, or utilize two parallel
decoders to detect individual objects and interactions separately, and compose
triplets by a matching process. In contrast, we decouple the triplet prediction
into human-object pair detection and interaction classification. Our main
motivation is that detecting the human-object instances and classifying
interactions accurately needs to learn representations that focus on different
regions. To this end, we present Disentangled Transformer, where both encoder
and decoder are disentangled to facilitate learning of two sub-tasks. To
associate the predictions of disentangled decoders, we first generate a unified
representation for HOI triplets with a base decoder, and then utilize it as
input feature of each disentangled decoder. Extensive experiments show that our
method outperforms prior work on two public HOI benchmarks by a sizeable
margin. Code will be available.
