Carl Doersch

According to our database, Carl Doersch authored at least 34 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

BootsTAP: Bootstrapped Training for Tracking-Any-Point.

Carl Doersch, Yi Yang, Dilara Gokay, Pauline Luc, Skanda Koppula, Ankush Gupta, Joseph Heyward, Ross Goroshin, João Carreira, Andrew Zisserman

CoRR, 2024

2023

Learning from One Continuous Video Stream.

João Carreira, Michael King, Viorica Patraucean, Dilara Gokay, Catalin Ionescu, Yi Yang, Daniel Zoran, Joseph Heyward, Carl Doersch, Yusuf Aytar, Dima Damen, Andrew Zisserman

CoRR, 2023

RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation.

Mel Vecerík, Carl Doersch, Yi Yang, Todor Davchev, Yusuf Aytar, Guangyao Zhou, Raia Hadsell, Lourdes Agapito, Jon Scholz

CoRR, 2023

Perception Test: A Diagnostic Benchmark for Multimodal Video Models.

Viorica Patraucean, Lucas Smaira, Ankush Gupta, Adrià Recasens, Larisa Markeeva, Dylan Banarse, Skanda Koppula, Joseph Heyward, Mateusz Malinowski, Yi Yang, Carl Doersch, Tatiana Matejovicova, Yury Sulsky, Antoine Miech, Alexandre Fréchette, Hanna Klimczak, Raphael Koster, Junlin Zhang, Stephanie Winkler, Yusuf Aytar, Simon Osindero, Dima Damen, Andrew Zisserman, João Carreira

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement.

Carl Doersch, Yi Yang, Mel Vecerík, Dilara Gokay, Ankush Gupta, Yusuf Aytar, João Carreira, Andrew Zisserman

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

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

Carl Doersch, Ankush Gupta, Larisa Markeeva, Adrià Recasens, Lucas Smaira, Yusuf Aytar, João Carreira, Andrew Zisserman, Yi Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Perceiver IO: A General Architecture for Structured Inputs & Outputs.

Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J. Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira

Proceedings of the Tenth International Conference on Learning Representations, 2022

Input-level Inductive Biases for 3D Reconstruction.

Wang Yifan, Carl Doersch, Relja Arandjelovic, João Carreira, Andrew Zisserman

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Kubric: A scalable dataset generator.

Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam H. Laradji, Hsueh-Ti Derek Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, A. Cengiz Öztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs.

Dan Rosenbaum, Marta Garnelo, Michal Zielinski, Charlie Beattie, Ellen Clancy, Andrea Huber, Pushmeet Kohli, Andrew W. Senior, John Jumper, Carl Doersch, S. M. Ali Eslami, Olaf Ronneberger, Jonas Adler

CoRR, 2021

2020

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning.

Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

CrossTransformers: spatially-aware few-shot transfer.

Carl Doersch, Ankush Gupta, Andrew Zisserman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Sim2real transfer learning for 3D pose estimation: motion to the rescue.

Carl Doersch, Andrew Zisserman

CoRR, 2019

Data-Efficient Image Recognition with Contrastive Predictive Coding.

Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aäron van den Oord

CoRR, 2019

Sim2real transfer learning for 3D human pose estimation: motion to the rescue.

Carl Doersch, Andrew Zisserman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Structured agents for physical construction.

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick

Proceedings of the 36th International Conference on Machine Learning, 2019

Video Action Transformer Network.

Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Exploiting Temporal Context for 3D Human Pose Estimation in the Wild.

Anurag Arnab, Carl Doersch, Andrew Zisserman

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR.

Mateusz Malinowski, Carl Doersch

CoRR, 2018

A Better Baseline for AVA.

Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman

CoRR, 2018

Kickstarting Deep Reinforcement Learning.

Simon Schmitt, Jonathan J. Hudson, Augustin Zídek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Küttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami

CoRR, 2018

Learning Visual Question Answering by Bootstrapping Hard Attention.

Mateusz Malinowski, Carl Doersch, Adam Santoro, Peter W. Battaglia

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

Multi-task Self-Supervised Visual Learning.

Carl Doersch, Andrew Zisserman

Proceedings of the IEEE International Conference on Computer Vision, 2017

2016

Supervision Beyond Manual Annotations for Learning Visual Representations.

Carl Doersch

PhD thesis, 2016

Data-dependent Initializations of Convolutional Neural Networks.

Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell

Proceedings of the 4th International Conference on Learning Representations, 2016

Tutorial on Variational Autoencoders.

Carl Doersch

CoRR, 2016

An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders.

Jacob Walker, Carl Doersch, Abhinav Gupta, Martial Hebert

Proceedings of the Computer Vision - ECCV 2016, 2016

2015

Mid-level Elements for Object Detection.

Aayush Bansal, Abhinav Shrivastava, Carl Doersch, Abhinav Gupta

CoRR, 2015

What makes Paris look like Paris?

Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei A. Efros

Commun. ACM, 2015

Unsupervised Visual Representation Learning by Context Prediction.

Carl Doersch, Abhinav Gupta, Alexei A. Efros

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014

Context as Supervisory Signal: Discovering Objects with Predictable Context.

Carl Doersch, Abhinav Gupta, Alexei A. Efros

Proceedings of the Computer Vision - ECCV 2014, 2014

2013

Mid-level Visual Element Discovery as Discriminative Mode Seeking.

Carl Doersch, Abhinav Gupta, Alexei A. Efros

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Bounding the Probability of Error for High Precision Optical Character Recognition.

Gary B. Huang, Andrew Kae, Carl Doersch, Erik G. Learned-Miller

J. Mach. Learn. Res., 2012

2010

Improving state-of-the-art OCR through high-precision document-specific modeling.

Andrew Kae, Gary B. Huang, Carl Doersch, Erik G. Learned-Miller

Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010