Visual Keyword Spotting with Attention

• URL: http://arxiv.org/abs/2110.15957v1
• Date: Fri, 29 Oct 2021 17:59:04 GMT
• Title: Visual Keyword Spotting with Attention
• Authors: K R Prajwal, Liliane Momeni, Triantafyllos Afouras, Andrew Zisserman
• Abstract summary: We investigate Transformer-based models that ingest two streams, a visual encoding of the video and a phonetic encoding of the keyword. We show through extensive evaluations that our model outperforms the prior state-of-the-art visual keyword spotting and lip reading methods. We demonstrate the ability of our model to spot words under the extreme conditions of isolated mouthings in sign language videos.
• Score: 82.79015266453533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we consider the task of spotting spoken keywords in silent video sequences -- also known as visual keyword spotting. To this end, we investigate Transformer-based models that ingest two streams, a visual encoding of the video and a phonetic encoding of the keyword, and output the temporal location of the keyword if present. Our contributions are as follows: (1) We propose a novel architecture, the Transpotter, that uses full cross-modal attention between the visual and phonetic streams; (2) We show through extensive evaluations that our model outperforms the prior state-of-the-art visual keyword spotting and lip reading methods on the challenging LRW, LRS2, LRS3 datasets by a large margin; (3) We demonstrate the ability of our model to spot words under the extreme conditions of isolated mouthings in sign language videos.

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