Search Results for author: Lucas Smaira

Found 10 papers, 7 papers with code

Zorro: the masked multimodal transformer

1 code implementation23 Jan 2023 Adrià Recasens, Jason Lin, Joāo Carreira, Drew Jaegle, Luyu Wang, Jean-Baptiste Alayrac, Pauline Luc, Antoine Miech, Lucas Smaira, Ross Hemsley, Andrew Zisserman

Attention-based models are appealing for multimodal processing because inputs from multiple modalities can be concatenated and fed to a single backbone network - thus requiring very little fusion engineering.

Audio Tagging Multimodal Deep Learning +2

TAP-Vid: A Benchmark for Tracking Any Point in a Video

2 code implementations7 Nov 2022 Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, Yi Yang

Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move.

Optical Flow Estimation

Human-Agent Cooperation in Bridge Bidding

no code implementations28 Nov 2020 Edward Lockhart, Neil Burch, Nolan Bard, Sebastian Borgeaud, Tom Eccles, Lucas Smaira, Ray Smith

We introduce a human-compatible reinforcement-learning approach to a cooperative game, making use of a third-party hand-coded human-compatible bot to generate initial training data and to perform initial evaluation.

Imitation Learning reinforcement-learning +1

A Short Note on the Kinetics-700-2020 Human Action Dataset

no code implementations21 Oct 2020 Lucas Smaira, João Carreira, Eric Noland, Ellen Clancy, Amy Wu, Andrew Zisserman

We describe the 2020 edition of the DeepMind Kinetics human action dataset, which replenishes and extends the Kinetics-700 dataset.

Self-Supervised MultiModal Versatile Networks

1 code implementation NeurIPS 2020 Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman

In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.

Action Recognition In Videos Audio Classification +2

Visual Grounding in Video for Unsupervised Word Translation

1 code implementation CVPR 2020 Gunnar A. Sigurdsson, Jean-Baptiste Alayrac, Aida Nematzadeh, Lucas Smaira, Mateusz Malinowski, João Carreira, Phil Blunsom, Andrew Zisserman

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.

Translation Visual Grounding +1

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