  Human-Object Interaction detection is a holistic visual recognition task that
entails object detection as well as interaction classification. Previous works
of HOI detection has been addressed by the various compositions of subset
predictions, e.g., Image -> HO -> I, Image -> HI -> O. Recently, transformer
based architecture for HOI has emerged, which directly predicts the HOI
triplets in an end-to-end fashion (Image -> HOI). Motivated by various
inference paths for HOI detection, we propose cross-path consistency learning
(CPC), which is a novel end-to-end learning strategy to improve HOI detection
for transformers by leveraging augmented decoding paths. CPC learning enforces
all the possible predictions from permuted inference sequences to be
consistent. This simple scheme makes the model learn consistent
representations, thereby improving generalization without increasing model
capacity. Our experiments demonstrate the effectiveness of our method, and we
achieved significant improvement on V-COCO and HICO-DET compared to the
baseline models. Our code is available at https://github.com/mlvlab/CPChoi.
