24 citations · 24 across the 5 of their papers we have counts for
6 papers
I Have a Stream: Making Self-Supervised Learning Work on Continuous Video
Ivan Martinović, Lukas Knobel, Yuki M. Asano
Self-supervised learning draws inspiration from infant visual development, yet standard training pipelines bear little resemblance to it: images are independently sampled and globa…
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…
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…
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
Walter Simoncini, Spyros Gidaris, Andrei Bursuc +1
This paper introduces FUNGI, Features from UNsupervised GradIents, a method to enhance the features of transformer encoders by leveraging self-supervised gradients. Our method is s…
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…
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video
Shashanka Venkataramanan, Mamshad Nayeem Rizve, João Carreira +2
Self-supervised learning has unlocked the potential of scaling up pretraining to billions of images, since annotation is unnecessary. But are we making the best use of data? How mo…