The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Ambient Sound Provides Supervision for Visual Learning
Abstract
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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Metadata
- Authors
- Owens, A., Wu, J., McDermott, J. H., Freeman, W. T., & Torralba, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 801-816
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2511428026
- Cited by
- 373
- References
- 13
- FWCI
- 14.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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