3 papers
cs.CV2026
Self-Supervised Learning of Structured Dynamics from Videos
Lukas Knobel, Andrew Zisserman, Yuki M. Asano
Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This deco…
cs.CV2026
Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
Shashanka Venkataramanan, Valentinos Pariza, Mohammadreza Salehi +5
We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance…
cs.CV2024
Self-supervised visual learning in the low-data regime: a comparative evaluation
Sotirios Konstantakos, Jorgen Cani, Ioannis Mademlis +4
Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a 'pretext task' tha…