  Human-object interactions (HOI) detection aims at capturing human-object
pairs in images and corresponding actions. It is an important step toward
high-level visual reasoning and scene understanding. However, due to the
natural bias from the real world, existing methods mostly struggle with rare
human-object pairs and lead to sub-optimal results. Recently, with the
development of the generative model, a straightforward approach is to construct
a more balanced dataset based on a group of supplementary samples.
Unfortunately, there is a significant domain gap between the generated data and
the original data, and simply merging the generated images into the original
dataset cannot significantly boost the performance. To alleviate the above
problem, we present a novel model-agnostic framework called
\textbf{C}ontext-\textbf{E}nhanced \textbf{F}eature \textbf{A}lignment (CEFA)
module, which can effectively align the generated data with the original data
at the feature level and bridge the domain gap. Specifically, CEFA consists of
a feature alignment module and a context enhancement module. On one hand,
considering the crucial role of human-object pairs information in HOI tasks,
the feature alignment module aligns the human-object pairs by aggregating
instance information. On the other hand, to mitigate the issue of losing
important context information caused by the traditional discriminator-style
alignment method, we employ a context-enhanced image reconstruction module to
improve the model's learning ability of contextual cues. Extensive experiments
have shown that our method can serve as a plug-and-play module to improve the
detection performance of HOI models on rare
categories\footnote{https://github.com/LijunZhang01/CEFA}.
