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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
Abstract:
Explaining the decision of a multi-modal decision-maker requires to determine
the evidence from both modalities. Recent advances in XAI provide explanations
for models trained on still images. However, when it comes to modeling multiple
sensory modalities in a dynamic world, it remains underexplored how to
demystify the mysterious dynamics of a complex multi-modal model. In this work,
we take a crucial step forward and explore learnable explanations for
audio-visual recognition. Specifically, we propose a novel space-time attention
network that uncovers the synergistic dynamics of audio and visual data over
both space and time. Our model is capable of predicting the audio-visual video
events, while justifying its decision by localizing where the relevant visual
cues appear, and when the predicted sounds occur in videos. We benchmark our
model on three audio-visual video event datasets, comparing extensively to
multiple recent multi-modal representation learners and intrinsic explanation
models. Experimental results demonstrate the clear superior performance of our
model over the existing methods on audio-visual video event recognition.
Moreover, we conduct an in-depth study to analyze the explainability of our
model based on robustness analysis via perturbation tests and pointing games
using human annotations.
