Convolutional networks trained on large supervised datasets produce visual features which form the basis for the state-of-the-art in many computer-vision problems. Further improvements of these visual features will likely require even larger manually labeled data sets, which severely limits the pace at which progress can be made. In this paper, we explore the potential of leveraging massive, weakly-labeled image collections for learning good visual features. We train convolutional networks on a dataset of 100 million Flickr photos and comments, and show that these networks produce features that perform well in a range of vision problems. We also show that the networks appropriately capture word similarity and learn correspondences between different languages.
CITATION STYLE
Joulin, A., van Der Maaten, L., Jabri, A., & Vasilache, N. (2016). Learning visual features from large weakly supervised data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9911 LNCS, pp. 67–84). Springer Verlag. https://doi.org/10.1007/978-3-319-46478-7_5
Mendeley helps you to discover research relevant for your work.