Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input
| Autor: | Galen Chuang, Antonio Torralba, Adrià Recasens, Dídac Surís, David Harwath, James Glass |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology computer.software_genre Object (computer science) Multimodal learning 030507 speech-language pathology & audiology 03 medical and health sciences Task (computing) Visual Objects Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science business computer Natural language processing Word (computer architecture) computer.programming_language |
| Zdroj: | Computer Vision – ECCV 2018 ISBN: 9783030012304 ECCV (6) |
| DOI: | 10.1007/978-3-030-01231-1_40 |
| Popis: | In this paper, we explore neural network models that learn to associate segments of spoken audio captions with the semantically relevant portions of natural images that they refer to. We demonstrate that these audio-visual associative localizations emerge from network-internal representations learned as a by-product of training to perform an image-audio retrieval task. Our models operate directly on the image pixels and speech waveform, and do not rely on any conventional supervision in the form of labels, segmentations, or alignments between the modalities during training. We perform analysis using the Places 205 and ADE20k datasets demonstrating that our models implicitly learn semantically-coupled object and word detectors. |
| Databáze: | OpenAIRE |
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