Human-Object Interaction (HOI) detection aims to understand the interactions
between humans and objects, which plays a curtail role in high-level semantic
understanding tasks. However, most works pursue designing better architectures
to learn overall features more efficiently, while ignoring the long-tail nature
of interaction-object pair categories. In this paper, we propose to alleviate
the impact of such an unbalanced distribution via Virtual Image Leaning (VIL).
Firstly, a novel label-to-image approach, Multiple Steps Image Creation
(MUSIC), is proposed to create a high-quality dataset that has a consistent
distribution with real images. In this stage, virtual images are generated
based on prompts with specific characterizations and selected by
multi-filtering processes. Secondly, we use both virtual and real images to
train the model with the teacher-student framework. Considering the initial
labels of some virtual images are inaccurate and inadequate, we devise an
Adaptive Matching-and-Filtering (AMF) module to construct pseudo-labels. Our
method is independent of the internal structure of HOI detectors, so it can be
combined with off-the-shelf methods by training merely 10 additional epochs.
With the assistance of our method, multiple methods obtain significant
improvements, and new state-of-the-art results are achieved on two benchmarks.

本文提出了一种基于虚拟图像学习的方法来解决人 - 物交互检测任务中类别分布不平衡的问题，通过借助虚拟图像和真实图像训练模型，构建伪标签以及结合现有方法进行训练，取得了显著改进并实现了新的最先进结果。