  We present a dual-pathway approach for recognizing fine-grained interactions
from videos. We build on the success of prior dual-stream approaches, but make
a distinction between the static and dynamic representations of objects and
their interactions explicit by introducing separate motion and object detection
pathways. Then, using our new Motion-Guided Attention Fusion module, we fuse
the bottom-up features in the motion pathway with features captured from object
detections to learn the temporal aspects of an action. We show that our
approach can generalize across appearance effectively and recognize actions
where an actor interacts with previously unseen objects. We validate our
approach using the compositional action recognition task from the
Something-Something-v2 dataset where we outperform existing state-of-the-art
methods. We also show that our method can generalize well to real world tasks
by showing state-of-the-art performance on recognizing humans assembling
various IKEA furniture on the IKEA-ASM dataset.
