3 papers
astro-ph.IM2026
Learning Radio Astronomical Representations with LeJEPA and Very Small Models
Erica Lastufka, Mariia Drozdova, Vitaliy Kinakh +4
Representations learned by vision foundation models pretrained on natural images have been shown to be useful for out-of-domain astronomical images. Performance on scientific downs…
astro-ph.IM2025
Radio Astronomy in the Era of Vision-Language Models: Prompt Sensitivity and Adaptation
Mariia Drozdova, Erica Lastufka, Vitaliy Kinakh +3
Vision-Language Models (VLMs), such as recent Qwen and Gemini models, are positioned as general-purpose AI systems capable of reasoning across domains. Yet their capabilities in sc…
astro-ph.IM2024
Bridging the Gap: Examining Vision Foundation Models for Optical and Radio Astronomy Applications
E. Lastufka, O. Bait, M. Drozdova +7
Vision foundation models, which have demonstrated significant potential in many multimedia applications, are often underutilized in the natural sciences. This is primarily due to m…