Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations

• URL: http://arxiv.org/abs/2409.07048v1
• Date: Wed, 11 Sep 2024 06:36:08 GMT
• Title: Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations
• Authors: Keumgang Cha, Donggeun Yu, Junghoon Seo,
• Abstract summary: This study introduces an approach to curate vision-language datasets by employing an image decoding machine learning model. We amassed approximately 9.6 million vision-language paired datasets in VHR imagery. The resultant model outperformed counterparts that did not leverage publicly available vision-language datasets.
• Score: 5.065947993017157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The prominence of generalized foundation models in vision-language integration has witnessed a surge, given their multifarious applications. Within the natural domain, the procurement of vision-language datasets to construct these foundation models is facilitated by their abundant availability and the ease of web crawling. Conversely, in the remote sensing domain, although vision-language datasets exist, their volume is suboptimal for constructing robust foundation models. This study introduces an approach to curate vision-language datasets by employing an image decoding machine learning model, negating the need for human-annotated labels. Utilizing this methodology, we amassed approximately 9.6 million vision-language paired datasets in VHR imagery. The resultant model outperformed counterparts that did not leverage publicly available vision-language datasets, particularly in downstream tasks such as zero-shot classification, semantic localization, and image-text retrieval. Moreover, in tasks exclusively employing vision encoders, such as linear probing and k-NN classification, our model demonstrated superior efficacy compared to those relying on domain-specific vision-language datasets.

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