1 research outputs found
Label Prediction Framework for Semi-Supervised Cross-Modal Retrieval
Cross-modal data matching refers to retrieval of data from one modality, when
given a query from another modality. In general, supervised algorithms achieve
better retrieval performance compared to their unsupervised counterpart, as
they can learn better representative features by leveraging the available label
information. However, this comes at the cost of requiring huge amount of
labeled examples, which may not always be available. In this work, we propose a
novel framework in a semi-supervised setting, which can predict the labels of
the unlabeled data using complementary information from different modalities.
The proposed framework can be used as an add-on with any baseline crossmodal
algorithm to give significant performance improvement, even in case of limited
labeled data. Finally, we analyze the challenging scenario where the unlabeled
examples can even come from classes not in the training data and evaluate the
performance of our algorithm under such setting. Extensive evaluation using
several baseline algorithms across three different datasets shows the
effectiveness of our label prediction framework.Comment: 12 pages, 3 tables, 2 figures, 1 algorithm flowchar