  Recognition and generation are two fundamental tasks in computer vision,
which are often investigated separately in the exiting literature. However,
these two tasks are highly correlated in essence as they both require
understanding the underline semantics of visual concepts. In this paper, we
propose a new learning framework, coined as CycleHOI, to boost the performance
of human-object interaction (HOI) detection by bridging the DETR-based
detection pipeline and the pre-trained text-to-image diffusion model. Our key
design is to introduce a novel cycle consistency loss for the training of HOI
detector, which is able to explicitly leverage the knowledge captured in the
powerful diffusion model to guide the HOI detector training. Specifically, we
build an extra generation task on top of the decoded instance representations
from HOI detector to enforce a detection-generation cycle consistency.
Moreover, we perform feature distillation from diffusion model to detector
encoder to enhance its representation power. In addition, we further utilize
the generation power of diffusion model to augment the training set in both
aspects of label correction and sample generation. We perform extensive
experiments to verify the effectiveness and generalization power of our
CycleHOI with three HOI detection frameworks on two public datasets: HICO-DET
and V-COCO. The experimental results demonstrate our CycleHOI can significantly
improve the performance of the state-of-the-art HOI detectors.
