miscellaneous
Cross-Modal Information Retrieval
3260 papers • 126 benchmarks • 313 datasets
Cross-Modal Information Retrieval (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the heterogeneity gap: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured. Source: Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
(Image credit: Papersgraph)
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iUse these libraries to find cross-modal-information-retrieval models and implementations
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CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
M. Datcu, Ushasi Chaudhuri, Biplab Banerjee, A. Bhattacharya•Mon Apr 08 2019
A novel deep neural network based architecture is proposed which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval.
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