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Thesis

Text-image synergy for multimodal retrieval and annotation

MPS-Authors
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Chowdhury,  Sreyasi Nag
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

External Resource

https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31690
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Citation

Chowdhury, S. N. (2021). Text-image synergy for multimodal retrieval and annotation. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-34509.


Cite as: https://hdl.handle.net/21.11116/0000-0009-428A-1
Abstract
Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.