1 code implementation • 25 Jul 2016 • Ankur Handa, Michael Bloesch, Viorica Patraucean, Simon Stent, John McCormac, Andrew Davison
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.
1 code implementation • 19 Nov 2015 • Viorica Patraucean, Ankur Handa, Roberto Cipolla
At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame.
1 code implementation • 22 Nov 2015 • Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, Roberto Cipolla
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
1 code implementation • ICCV 2021 • 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.
no code implementations • ECCV 2018 • Joao Carreira, Viorica Patraucean, Laurent Mazare, Andrew Zisserman, Simon Osindero
We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number of clock cycles.
no code implementations • 1 May 2015 • Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, Roberto Cipolla
We are interested in automatic scene understanding from geometric cues.
no code implementations • CVPR 2016 • Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, Roberto Cipolla
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
no code implementations • CVPR 2020 • Mateusz Malinowski, Grzegorz Swirszcz, Joao Carreira, Viorica Patraucean
We propose Sideways, an approximate backpropagation scheme for training video models.
no code implementations • CVPR 2021 • Mateusz Malinowski, Dimitrios Vytiniotis, Grzegorz Swirszcz, Viorica Patraucean, Joao Carreira
How can neural networks be trained on large-volume temporal data efficiently?
no code implementations • 9 Nov 2022 • Jannik Kossen, Cătălina Cangea, Eszter Vértes, Andrew Jaegle, Viorica Patraucean, Ira Ktena, Nenad Tomasev, Danielle Belgrave
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT).
no code implementations • 12 Dec 2023 • Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak, Justin Chiu, Joe Heyward, Viorica Patraucean, Jiajun Shen, Antoine Miech, Andrew Zisserman, Aida Nematzdeh
Understanding long, real-world videos requires modeling of long-range visual dependencies.