Search Results for author: Aravindh Mahendran

Found 11 papers, 3 papers with code

Object Scene Representation Transformer

no code implementations14 Jun 2022 Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetić, Mario Lučić, Leonidas J. Guibas, Klaus Greff, Thomas Kipf

A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition.

Novel View Synthesis Representation Learning

Conditional Object-Centric Learning from Video

no code implementations ICLR 2022 Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, Klaus Greff

Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.

Instance Segmentation Optical Flow Estimation +2

Differentiable Patch Selection for Image Recognition

no code implementations CVPR 2021 Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner

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.

Traffic Sign Recognition

Object-Centric Learning with Slot Attention

4 code implementations NeurIPS 2020 Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features.

Object Discovery

Self-Supervised Learning of Video-Induced Visual Invariances

no code implementations CVPR 2020 Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Xiaohua Zhai, Neil Houlsby, Sylvain Gelly, Mario Lucic

We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI).

Ranked #14 on Image Classification on VTAB-1k (using extra training data)

Image Classification Self-Supervised Learning +1

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

no code implementations15 Jul 2018 Aravindh Mahendran, James Thewlis, Andrea Vedaldi

We propose a novel method for learning convolutional neural image representations without manual supervision.

Image Classification Optical Flow Estimation +2

Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images

no code implementations7 Dec 2015 Aravindh Mahendran, Andrea Vedaldi

Image representations, from SIFT and bag of visual words to Convolutional Neural Networks (CNNs) are a crucial component of almost all computer vision systems.

Understanding Deep Image Representations by Inverting Them

7 code implementations CVPR 2015 Aravindh Mahendran, Andrea Vedaldi

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system.

Cannot find the paper you are looking for? You can Submit a new open access paper.