  Cameras are essential vision instruments to capture images for pattern
detection and measurement. Human-object interaction (HOI) detection is one of
the most popular pattern detection approaches for captured human-centric visual
scenes. Recently, Transformer-based models have become the dominant approach
for HOI detection due to their advanced network architectures and thus
promising results. However, most of them follow the one-stage design of vanilla
Transformer, leaving rich geometric priors under-exploited and leading to
compromised performance especially when occlusion occurs. Given that geometric
features tend to outperform visual ones in occluded scenarios and offer
information that complements visual cues, we propose a novel end-to-end
Transformer-style HOI detection model, i.e., geometric features enhanced HOI
detector (GeoHOI). One key part of the model is a new unified self-supervised
keypoint learning method named UniPointNet that bridges the gap of consistent
keypoint representation across diverse object categories, including humans.
GeoHOI effectively upgrades a Transformer-based HOI detector benefiting from
the keypoints similarities measuring the likelihood of human-object
interactions as well as local keypoint patches to enhance interaction query
representation, so as to boost HOI predictions. Extensive experiments show that
the proposed method outperforms the state-of-the-art models on V-COCO and
achieves competitive performance on HICO-DET. Case study results on the
post-disaster rescue with vision-based instruments showcase the applicability
of the proposed GeoHOI in real-world applications.
