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Label Prediction Framework for Semi-Supervised Cross-Modal Retrieval

Mandal, D and Rao, P and Biswas, S (2020) Label Prediction Framework for Semi-Supervised Cross-Modal Retrieval. In: Proceedings - International Conference on Image Processing, ICIP, 25-28 September 2020, Virtual, Abu Dhabi; United Arab Emirates, pp. 2311-2315.

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Official URL: https://dx.doi.org/10.1109/ICIP40778.2020.9190722


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 cross-modal retrieval 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 cross-modal algorithm to give significant performance improvement, even in case of limited labeled data. Extensive evaluation using several baseline algorithms across three different datasets show the effectiveness of our label prediction framework. © 2020 IEEE.

Item Type: Conference Paper
Publication: Proceedings - International Conference on Image Processing, ICIP
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference Date: 25 September 2020 Through 28 September 2020; Conference Code:165772
Keywords: Forecasting; Modal analysis, Cross-modal; Label information; Label predictions; Retrieval performance; Semi-supervised; Supervised algorithm; Unlabeled data, Image processing
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 02 Feb 2021 10:25
Last Modified: 02 Feb 2021 10:25
URI: http://eprints.iisc.ac.in/id/eprint/67727

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