Fast-Slow Transformer for Visually Grounding Speech

• URL: http://arxiv.org/abs/2109.08186v1
• Date: Thu, 16 Sep 2021 18:45:45 GMT
• Title: Fast-Slow Transformer for Visually Grounding Speech
• Authors: Puyuan Peng and David Harwath
• Abstract summary: We present Fast-Slow Transformer for Visually Grounding Speech, or FaST-VGS. FaST-VGS is a Transformer-based model for learning the associations between raw speech waveforms and visual images.
• Score: 15.68151998164009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Fast-Slow Transformer for Visually Grounding Speech, or FaST-VGS. FaST-VGS is a Transformer-based model for learning the associations between raw speech waveforms and visual images. The model unifies dual-encoder and cross-attention architectures into a single model, reaping the superior retrieval speed of the former along with the accuracy of the latter. FaST-VGS achieves state-of-the-art speech-image retrieval accuracy on benchmark datasets, and its learned representations exhibit strong performance on the ZeroSpeech 2021 phonetic and semantic tasks.

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