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

Motion Guided Attention Fusion to Recognize Interactions from Videos