Menon, AdityaSurian, DidiChawla, Sanjay2016-06-14April 30 t9781611974010http://hdl.handle.net/1885/103814Content is increasingly available in multiple modalities (such as images, text, and video), each of which provides a different representation of some entity. The cross-modal retrieval problem is: given the representation of an entity in one modality, find its best representation in all other modalities. We propose a novel approach to this problem based on pairwise classification. The approach seamlessly applies to both the settings where ground-truth annotations for the entities are absent and present. In the former case, the approach considers both positive and unlabelled links that arise in standard cross-modal retrieval datasets. Empirical comparisons show improvements over state-of-the-art methods for cross-modal retrieval201510.1137/1.9781611974010.232016-06-14