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arXiv · 1705.08631

Self-supervised learning of visual features through embedding images into text topic spaces

Lluis Gomez·Yash Patel·Marçal Rusiñol·Dimosthenis Karatzas·C. V. Jawahar
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Abstract

End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.

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Lluis Gomez, Yash Patel, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar. 2017-05-24. Self-supervised learning of visual features through embedding images into text topic spaces. https://arxiv.org/abs/1705.08631

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