Search Results for author: Aravindh Mahendran

Found 15 papers, 8 papers with code

Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames

1 code implementation9 Feb 2023 Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin F. Elsayed, Aravindh Mahendran, Thomas Kipf

Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning.

Object Object Discovery

RUST: Latent Neural Scene Representations from Unposed Imagery

no code implementations CVPR 2023 Mehdi S. M. Sajjadi, Aravindh Mahendran, Thomas Kipf, Etienne Pot, Daniel Duckworth, Mario Lucic, Klaus Greff

Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis.

Novel View Synthesis

Iterative Patch Selection for High-Resolution Image Recognition

1 code implementation24 Oct 2022 Benjamin Bergner, Christoph Lippert, Aravindh Mahendran

We propose a simple method, Iterative Patch Selection (IPS), which decouples the memory usage from the input size and thus enables the processing of arbitrarily large images under tight hardware constraints.

Autonomous Driving Multiple Instance Learning +2

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 Object +1

Conditional Object-Centric Learning from Video

3 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 Object +3

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

8 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 Object Discovery +1

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 #15 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 Image Segmentation +4

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

8 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.

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