Gamaleldin Fathy Elsayed — accepted AI-conference papers

2023

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

Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf

ICML 2023poster

Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this direction. However, they typically fall short at ade…

Cited by 31 Source ↗PDF ↗code ↗Scholar ↗

2023

Learning in temporally structured environments

Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin Fathy Elsayed, Katherine Hermann, David Mayo +1

ICLR 2023poster

Natural environments have temporal structure at multiple timescales. This property is reflected in biological learning and memory but typically not in machine learning systems. We advance a multiscale learning method in which each weight in a neural network is decomposed as a sum of subweights with…

Cited by 6 Source ↗PDF ↗Scholar ↗

2023

Scaling Vision Transformers to 22 Billion Parameters

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer +36

ICML 2023oral

The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been suc…

Cited by 650 Source ↗PDF ↗Scholar ↗

2022

Conditional Object-Centric Learning from Video

Thomas Kipf, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold +3

ICLR 2022poster

Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Recent work on simple 2D and 3D datasets has shown that models with object-centric inductive biases can learn to segment an…

Cited by 239 Source ↗PDF ↗code ↗Scholar ↗

2022

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos

Gamaleldin Fathy Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael Curtis Mozer, Thomas Kipf

NeurIPS 2022accept

The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless explicit instance-level supervision is provided. Slot…

Cited by 154 Source ↗PDF ↗Scholar ↗