Ambient Sound Provides Supervision for Visual Learning

author: Andrew Owens, Department of Electrical Engineering and Computer Sciences, UC Berkeley
published: Oct. 24, 2016,   recorded: October 2016,   views: 7
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Description

The sound of crashing waves, the roar of fast-moving cars -- sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.

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Download slides icon Download slides: eccv2016_owens_ambient_sound_01.pdf (11.0┬áMB)


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