  Recent one-stage transformer-based methods achieve notable gains in the
Human-object Interaction Detection (HOI) task by leveraging the detection of
DETR. However, the current methods redirect the detection target of the object
decoder, and the box target is not explicitly separated from the query
embeddings, which leads to long and hard training. Furthermore, matching the
predicted HOI instances with the ground-truth is more challenging than object
detection, simply adapting training strategies from the object detection makes
the training more difficult. To clear the ambiguity between human and object
detection and share the prediction burden, we propose a novel one-stage
framework (SOV), which consists of a subject decoder, an object decoder, and a
verb decoder. Moreover, we propose a novel Specific Target Guided (STG)
DeNoising training strategy, which leverages learnable object and verb label
embeddings to guide the training and accelerate the training convergence. In
addition, for the inference part, the label-specific information is directly
fed into the decoders by initializing the query embeddings from the learnable
label embeddings. Without additional features or prior language knowledge, our
method (SOV-STG) achieves higher accuracy than the state-of-the-art method in
one-third of training epochs. The code is available at this
https://github.com/cjw2021/SOV-STG.
