FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing

• URL: http://arxiv.org/abs/2501.08490v1
• Date: Tue, 14 Jan 2025 23:31:20 GMT
• Title: FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing
• Authors: Isaac Corley, Simone Fobi Nsutezo, Anthony Ortiz, Caleb Robinson, Rahul Dodhia, Juan M. Lavista Ferres, Peyman Najafirad,
• Abstract summary: We propose FLAVARS, a pretraining method that combines contrastive learning and masked modeling. We find that FLAVARS significantly outperforms a baseline of SkyCLIP for vision-only tasks such as KNN classification.
• Score: 5.170800801074805
• License:
• Abstract: Remote sensing imagery is dense with objects and contextual visual information. There is a recent trend to combine paired satellite images and text captions for pretraining performant encoders for downstream tasks. However, while contrastive image-text methods like CLIP enable vision-language alignment and zero-shot classification ability, vision-only downstream performance tends to degrade compared to image-only pretraining, such as MAE. In this paper, we propose FLAVARS, a pretraining method that combines the best of both contrastive learning and masked modeling, along with geospatial alignment via contrastive location encoding. We find that FLAVARS significantly outperforms a baseline of SkyCLIP for vision-only tasks such as KNN classification and semantic segmentation, +6\% mIOU on SpaceNet1, while retaining the ability to perform zero-shot classification, unlike MAE pretrained methods.

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