  Detecting human-object interaction (HOI) has long been limited by the amount
of supervised data available. Recent approaches address this issue by
pre-training according to pseudo-labels, which align object regions with HOI
triplets parsed from image captions. However, pseudo-labeling is tricky and
noisy, making HOI pre-training a complex process. Therefore, we propose an
efficient disentangled pre-training method for HOI detection (DP-HOI) to
address this problem. First, DP-HOI utilizes object detection and action
recognition datasets to pre-train the detection and interaction decoder layers,
respectively. Then, we arrange these decoder layers so that the pre-training
architecture is consistent with the downstream HOI detection task. This
facilitates efficient knowledge transfer. Specifically, the detection decoder
identifies reliable human instances in each action recognition dataset image,
generates one corresponding query, and feeds it into the interaction decoder
for verb classification. Next, we combine the human instance verb predictions
in the same image and impose image-level supervision. The DP-HOI structure can
be easily adapted to the HOI detection task, enabling effective model parameter
initialization. Therefore, it significantly enhances the performance of
existing HOI detection models on a broad range of rare categories. The code and
pre-trained weight are available at https://github.com/xingaoli/DP-HOI.
