7 citations · 14 across the 8 of their papers we have counts for
4 papers · 1 filter
Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
Mariia Drozdova, Vitaliy Kinakh, Yury Belousov +2
In this paper, we present a semi-supervised fine-tuning approach designed to improve the performance of pre-trained foundation models on downstream tasks with limited labeled data.…
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…
Self-Supervised Learning on MeerKAT Wide-Field Continuum Images
Erica Lastufka, Omkar Bait, Olga Taran +8
Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in model…
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model
Mariia Drozdova, Vitaliy Kinakh, Omkar Bait +6
Reconstructing sky models from dirty radio images for accurate source localization and flux estimation is crucial for studying galaxy evolution at high redshift, especially in deep…