Image-text retrieval (ITR) aims to retrieve images or texts that match a query originating from
the other modality. The conventional dense retrieval paradigm relies on encoding images …

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …

In this work, we present a post-processing solution to address the hubness problem in cross-
modal retrieval, a phenomenon where a small number of gallery data points are frequently …

Image-text retrieval (ITR) is a task to retrieve the relevant images/texts, given the query from
another modality. The conventional dense retrieval paradigm relies on encoding images …

Cross-modal image-text retrieval is a crucial task in the field of vision and language, aimed
at retrieving the relevant samples from one modality as per the given user expressed in …

Remote sensing offers vast yet sparsely labeled multimodal data but lacks foundation
models that can be leveraged across societally impactful applications. In this thesis, I …