Watch, read and lookup: learning to spot signs from multiple supervisors

• URL: http://arxiv.org/abs/2010.04002v1
• Date: Thu, 8 Oct 2020 14:12:56 GMT
• Title: Watch, read and lookup: learning to spot signs from multiple supervisors
• Authors: Liliane Momeni, G\"ul Varol, Samuel Albanie, Triantafyllos Afouras, Andrew Zisserman
• Abstract summary: Given a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video. We train a model using multiple types of available supervision by: (1) watching existing sparsely labelled footage; (2) reading associated subtitles which provide additional weak-supervision; and (3) looking up words in visual sign language dictionaries. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning.
• Score: 99.50956498009094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The focus of this work is sign spotting - given a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) watching existing sparsely labelled footage; (2) reading associated subtitles (readily available translations of the signed content) which provide additional weak-supervision; (3) looking up words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, BSLDict, to facilitate study of this task. The dataset, models and code are available at our project page.

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