  Detecting human interactions is crucial for human behavior analysis. Many
methods have been proposed to deal with Human-to-Object Interaction (HOI)
detection, i.e., detecting in an image which person and object interact
together and classifying the type of interaction. However, Human-to-Human
Interactions, such as social and violent interactions, are generally not
considered in available HOI training datasets. As we think these types of
interactions cannot be ignored and decorrelated from HOI when analyzing human
behavior, we propose a new interaction dataset to deal with both types of human
interactions: Human-to-Human-or-Object (H2O). In addition, we introduce a novel
taxonomy of verbs, intended to be closer to a description of human body
attitude in relation to the surrounding targets of interaction, and more
independent of the environment. Unlike some existing datasets, we strive to
avoid defining synonymous verbs when their use highly depends on the target
type or requires a high level of semantic interpretation. As H2O dataset
includes V-COCO images annotated with this new taxonomy, images obviously
contain more interactions. This can be an issue for HOI detection methods whose
complexity depends on the number of people, targets or interactions. Thus, we
propose DIABOLO (Detecting InterActions By Only Looking Once), an efficient
subject-centric single-shot method to detect all interactions in one forward
pass, with constant inference time independent of image content. In addition,
this multi-task network simultaneously detects all people and objects. We show
how sharing a network for these tasks does not only save computation resource
but also improves performance collaboratively. Finally, DIABOLO is a strong
baseline for the new proposed challenge of H2O Interaction detection, as it
outperforms all state-of-the-art methods when trained and evaluated on HOI
dataset V-COCO.
