  Person-person mutual action recognition (also referred to as interaction
recognition) is an important research branch of human activity analysis.
Current solutions in the field -- mainly dominated by CNNs, GCNs and LSTMs --
often consist of complicated architectures and mechanisms to embed the
relationships between the two persons on the architecture itself, to ensure the
interaction patterns can be properly learned. Our main contribution with this
work is by proposing a simpler yet very powerful architecture, named
Interaction Relational Network, which utilizes minimal prior knowledge about
the structure of the human body. We drive the network to identify by itself how
to relate the body parts from the individuals interacting. In order to better
represent the interaction, we define two different relationships, leading to
specialized architectures and models for each. These multiple relationship
models will then be fused into a single and special architecture, in order to
leverage both streams of information for further enhancing the relational
reasoning capability. Furthermore we define important structured pair-wise
operations to extract meaningful extra information from each pair of joints --
distance and motion. Ultimately, with the coupling of an LSTM, our IRN is
capable of paramount sequential relational reasoning. These important
extensions we made to our network can also be valuable to other problems that
require sophisticated relational reasoning. Our solution is able to achieve
state-of-the-art performance on the traditional interaction recognition
datasets SBU and UT, and also on the mutual actions from the large-scale
dataset NTU RGB+D. Furthermore, it obtains competitive performance in the NTU
RGB+D 120 dataset interactions subset.
