  In this paper, we introduce a novel human interaction detection approach,
based on CALIPSO (Classifying ALl Interacting Pairs in a Single shOt), a
classifier of human-object interactions. This new single-shot interaction
classifier estimates interactions simultaneously for all human-object pairs,
regardless of their number and class. State-of-the-art approaches adopt a
multi-shot strategy based on a pairwise estimate of interactions for a set of
human-object candidate pairs, which leads to a complexity depending, at least,
on the number of interactions or, at most, on the number of candidate pairs. In
contrast, the proposed method estimates the interactions on the whole image.
Indeed, it simultaneously estimates all interactions between all human subjects
and object targets by performing a single forward pass throughout the image.
Consequently, it leads to a constant complexity and computation time
independent of the number of subjects, objects or interactions in the image. In
detail, interaction classification is achieved on a dense grid of anchors
thanks to a joint multi-task network that learns three complementary tasks
simultaneously: (i) prediction of the types of interaction, (ii) estimation of
the presence of a target and (iii) learning of an embedding which maps
interacting subject and target to a same representation, by using a metric
learning strategy. In addition, we introduce an object-centric passive-voice
verb estimation which significantly improves results. Evaluations on the two
well-known Human-Object Interaction image datasets, V-COCO and HICO-DET,
demonstrate the competitiveness of the proposed method (2nd place) compared to
the state-of-the-art while having constant computation time regardless of the
number of objects and interactions in the image.
