  Human-object interaction (HOI) detection is a core task in computer vision.
The goal is to localize all human-object pairs and recognize their
interactions. An interaction defined by a <verb, noun> tuple leads to a
long-tailed visual recognition challenge since many combinations are rarely
represented. The performance of the proposed models is limited especially for
the tail categories, but little has been done to understand the reason. To that
end, in this paper, we propose to diagnose rarity in HOI detection. We propose
a three-step strategy, namely Detection, Identification and Recognition where
we carefully analyse the limiting factors by studying state-of-the-art models.
Our findings indicate that detection and identification steps are altered by
the interaction signals like occlusion and relative location, as a result
limiting the recognition accuracy.
