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arXiv · 1905.13339

Multitask Text-to-Visual Embedding with Titles and Clickthrough Data

Pranav Aggarwal·Zhe Lin·Baldo Faieta·Saeid Motiian
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Abstract

Text-visual (or called semantic-visual) embedding is a central problem in vision-language research. It typically involves mapping of an image and a text description to a common feature space through a CNN image encoder and a RNN language encoder. In this paper, we propose a new method for learning text-visual embedding using both image titles and click-through data from an image search engine. We also propose a new triplet loss function by modeling positive awareness of the embedding, and introduce a novel mini-batch-based hard negative sampling approach for better data efficiency in the learning process. Experimental results show that our proposed method outperforms existing methods, and is also effective for real-world text-to-visual retrieval.

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Pranav Aggarwal, Zhe Lin, Baldo Faieta, Saeid Motiian. 2019-05-30. Multitask Text-to-Visual Embedding with Titles and Clickthrough Data. https://arxiv.org/abs/1905.13339

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