5 papers
Differentiable Patch Selection for Image Recognition
Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy +3
Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand…
Representation learning from videos in-the-wild: An object-centric approach
Rob Romijnders, Aravindh Mahendran, Michael Tschannen +4
We propose a method to learn image representations from uncurated videos. We combine a supervised loss from off-the-shelf object detectors and self-supervised losses which naturall…
Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner +5
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learn…
Self-Supervised Learning of Video-Induced Visual Invariances
Michael Tschannen, Josip Djolonga, Marvin Ritter +5
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI). We consider the implicit hierar…
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
Aravindh Mahendran, James Thewlis, Andrea Vedaldi
We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise represent…