Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment

• URL: http://arxiv.org/abs/2402.09816v1
• Date: Thu, 15 Feb 2024 09:31:07 GMT
• Title: Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
• Authors: Angelos Zavras, Dimitrios Michail, Beg\"um Demir, Ioannis Papoutsis
• Abstract summary: We focus on Contrastive Language-Image Pre-training (CLIP), an open-vocabulary foundation model, which achieves high accuracy across many image classification tasks. There are still domains where zero-shot CLIP performance is far from optimal, such as Remote Sensing (RS) and medical imagery. We propose a methodology for the purpose of aligning distinct RS imagery modalities with the visual and textual modalities of CLIP.
• Score: 2.389598109913754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Learning (DL) is undergoing a paradigm shift with the emergence of foundation models, aptly named by their crucial, yet incomplete nature. In this work, we focus on Contrastive Language-Image Pre-training (CLIP), an open-vocabulary foundation model, which achieves high accuracy across many image classification tasks and is often competitive with a fully supervised baseline without being explicitly trained. Nevertheless, there are still domains where zero-shot CLIP performance is far from optimal, such as Remote Sensing (RS) and medical imagery. These domains do not only exhibit fundamentally different distributions compared to natural images, but also commonly rely on complementary modalities, beyond RGB, to derive meaningful insights. To this end, we propose a methodology for the purpose of aligning distinct RS imagery modalities with the visual and textual modalities of CLIP. Our two-stage procedure, comprises of robust fine-tuning CLIP in order to deal with the distribution shift, accompanied by the cross-modal alignment of a RS modality encoder, in an effort to extend the zero-shot capabilities of CLIP. We ultimately demonstrate our method on the tasks of RS imagery classification and cross-modal retrieval. We empirically show that both robust fine-tuning and cross-modal alignment translate to significant performance gains, across several RS benchmark datasets. Notably, these enhancements are achieved without the reliance on textual descriptions, without introducing any task-specific parameters, without training from scratch and without catastrophic forgetting.

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