  The interactions between human and objects are important for recognizing
object-centric actions. Existing methods usually adopt a two-stage pipeline,
where object proposals are first detected using a pretrained detector, and then
are fed to an action recognition model for extracting video features and
learning the object relations for action recognition. However, since the action
prior is unknown in the object detection stage, important objects could be
easily overlooked, leading to inferior action recognition performance. In this
paper, we propose an end-to-end object-centric action recognition framework
that simultaneously performs Detection And Interaction Reasoning in one stage.
Particularly, after extracting video features with a base network, we create
three modules for concurrent object detection and interaction reasoning. First,
a Patch-based Object Decoder generates proposals from video patch tokens. Then,
an Interactive Object Refining and Aggregation identifies important objects for
action recognition, adjusts proposal scores based on position and appearance,
and aggregates object-level info into a global video representation. Lastly, an
Object Relation Modeling module encodes object relations. These three modules
together with the video feature extractor can be trained jointly in an
end-to-end fashion, thus avoiding the heavy reliance on an off-the-shelf object
detector, and reducing the multi-stage training burden. We conduct experiments
on two datasets, Something-Else and Ikea-Assembly, to evaluate the performance
of our proposed approach on conventional, compositional, and few-shot action
recognition tasks. Through in-depth experimental analysis, we show the crucial
role of interactive objects in learning for action recognition, and we can
outperform state-of-the-art methods on both datasets.
