We introduce SODA, a self-supervised diffusion model, designed for representation learning.
This work shows that current code generation systems exhibit undesired biases inherited from their large language model backbones, which can reduce the quality of the generated code under specific circumstances.
1 code implementation • • Viorica Pătrăucean, Lucas Smaira, Ankush Gupta, Adrià Recasens Continente, Larisa Markeeva, Dylan Banarse, Mateusz Malinowski, Yi Yang, Carl Doersch, Tatiana Matejovicova, Yury Sulsky, Antoine Miech, Skanda Koppula, Alex Frechette, Hanna Klimczak, Raphael Koster, Junlin Zhang, Stephanie Winkler, Yusuf Aytar, Simon Osindero, Dima Damen, Andrew Zisserman and João Carreira
We propose a novel multimodal benchmark – the Perception Test – that aims to extensively evaluate perception and reasoning skills of multimodal models.
In this work, we propose a framework enabling research on hour-long videos with the same hardware that can now process second-long videos.
Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team.
The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art.
We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction.
Visual question answering provides a convenient framework for testing the model's abilities by interrogating the model through questions about the scene.
3 code implementations • 15 Feb 2022 • Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse Engel
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression.
To answer such a question, we extend the visual question answering framework and propose the following behavioral test in the form of a two-player game.
Such an approach assumes that other agents' goals are known so that the altruistic agent can cooperate in achieving those goals.
How can neural networks be trained on large-volume temporal data efficiently?
1 code implementation • • Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Ross Hemsley, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Patraucean, Florent Altché, Michal Valko, Jean-bastien Grill, Aäron van den Oord, Andrew Zisserman
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data.
Ranked #1 on Self-Supervised Audio Classification on ESC-50
In this work, we investigate the problem of revealing the functionality of a black-box agent.
Given this shared embedding we demonstrate that (i) we can map words between the languages, particularly the 'visual' words; (ii) that the shared embedding provides a good initialization for existing unsupervised text-based word translation techniques, forming the basis for our proposed hybrid visual-text mapping algorithm, MUVE; and (iii) our approach achieves superior performance by addressing the shortcomings of text-based methods -- it is more robust, handles datasets with less commonality, and is applicable to low-resource languages.
1 code implementation • 4 Mar 2019 • Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Denis Teplyashin, Karl Moritz Hermann, Mateusz Malinowski, Matthew Koichi Grimes, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell
These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported.
Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision.
In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction.
We study the problem of learning classifiers robust to universal adversarial perturbations.
Visual QA is a pivotal challenge for higher-level reasoning, requiring understanding language, vision, and relationships between many objects in a scene.
Attention mechanisms in biological perception are thought to select subsets of perceptual information for more sophisticated processing which would be prohibitive to perform on all sensory inputs.
Ranked #7 on Visual Question Answering (VQA) on CLEVR
30 code implementations • 4 Jun 2018 • Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
no code implementations • • Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas
We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure.
3 code implementations • • Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell
We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away.
Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
Boundary estimation in images and videos has been a very active topic of research, and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception.
Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged.
We present Mean Box Pooling, a novel visual representation that pools over CNN representations of a large number, highly overlapping object proposals.
Furthermore, we show long-term prediction of boundaries in situations where the motion is governed by the laws of physics.
By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem.
A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes.
We show that our retrieval system can cope with this variability using personalisation through an online learning-based retrieval formulation.
In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language input (image and question).
Progress in language and image understanding by machines has sparkled the interest of the research community in more open-ended, holistic tasks, and refueled an old AI dream of building intelligent machines.
Over the last two decades we have witnessed strong progress on modeling visual object classes, scenes and attributes that have significantly contributed to automated image understanding.
As language and visual understanding by machines progresses rapidly, we are observing an increasing interest in holistic architectures that tightly interlink both modalities in a joint learning and inference process.
We propose a method for automatically answering questions about images by bringing together recent advances from natural language processing and computer vision.
Biologically inspired, from the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines.