  Video Question Answering is a task which requires an AI agent to answer
questions grounded in video. This task entails three key challenges: (1)
understand the intention of various questions, (2) capturing various elements
of the input video (e.g., object, action, causality), and (3) cross-modal
grounding between language and vision information. We propose Motion-Appearance
Synergistic Networks (MASN), which embed two cross-modal features grounded on
motion and appearance information and selectively utilize them depending on the
question's intentions. MASN consists of a motion module, an appearance module,
and a motion-appearance fusion module. The motion module computes the
action-oriented cross-modal joint representations, while the appearance module
focuses on the appearance aspect of the input video. Finally, the
motion-appearance fusion module takes each output of the motion module and the
appearance module as input, and performs question-guided fusion. As a result,
MASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA
datasets. We also conduct qualitative analysis by visualizing the inference
results of MASN. The code is available at
https://github.com/ahjeongseo/MASN-pytorch.
