  Human-object interaction recognition aims for identifying the relationship
between a human subject and an object. Researchers incorporate global scene
context into the early layers of deep Convolutional Neural Networks as a
solution. They report a significant increase in the performance since generally
interactions are correlated with the scene (\ie riding bicycle on the city
street). However, this approach leads to the following problems. It increases
the network size in the early layers, therefore not efficient. It leads to
noisy filter responses when the scene is irrelevant, therefore not accurate. It
only leverages scene context whereas human-object interactions offer a
multitude of contexts, therefore incomplete. To circumvent these issues, in
this work, we propose Self-Selective Context (SSC). SSC operates on the joint
appearance of human-objects and context to bring the most discriminative
context(s) into play for recognition. We devise novel contextual features that
model the locality of human-object interactions and show that SSC can
seamlessly integrate with the State-of-the-art interaction recognition models.
Our experiments show that SSC leads to an important increase in interaction
recognition performance, while using much fewer parameters.
