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Convolutional networks trained on large supervised dataset produce visualfeatures which form the basis for the state-of-the-art in many computer-visionproblems. Further improvements of these visual features will likely requireeven larger manually labeled data sets, which severely limits the pace at whichprogress can be made. In this paper, we explore the potential of leveragingmassive, weakly-labeled image collections for learning good visual features. Wetrain convolutional networks on a dataset of 100 million Flickr photos andcaptions, and show that these networks produce features that perform well in arange of vision problems. We also show that the networks appropriately captureword similarity, and learn correspondences between different languages.

Learning Visual Features from Large Weakly Supervised Data