Carl Doersch

According to our database1, Carl Doersch authored at least 20 papers between 2010 and 2018.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2018
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR.
CoRR, 2018

Learning Visual Question Answering by Bootstrapping Hard Attention.
CoRR, 2018

A Better Baseline for AVA.
CoRR, 2018

Kickstarting Deep Reinforcement Learning.
CoRR, 2018

Learning Visual Question Answering by Bootstrapping Hard Attention.
Proceedings of the Computer Vision - ECCV 2018, 2018

2017
Multi-task Self-Supervised Visual Learning.
CoRR, 2017

Multi-task Self-Supervised Visual Learning.
Proceedings of the IEEE International Conference on Computer Vision, 2017

2016
An Uncertain Future: Forecasting from Static Images using Variational Autoencoders.
CoRR, 2016

Tutorial on Variational Autoencoders.
CoRR, 2016

An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders.
Proceedings of the Computer Vision - ECCV 2016, 2016

2015
Data-dependent Initializations of Convolutional Neural Networks.
CoRR, 2015

Unsupervised Visual Representation Learning by Context Prediction.
CoRR, 2015

Mid-level Elements for Object Detection.
CoRR, 2015

What makes Paris look like Paris?
Commun. ACM, 2015

Unsupervised Visual Representation Learning by Context Prediction.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014
Context as Supervisory Signal: Discovering Objects with Predictable Context.
Proceedings of the Computer Vision - ECCV 2014, 2014

2013
Mid-level Visual Element Discovery as Discriminative Mode Seeking.
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
What makes Paris look like Paris?
ACM Trans. Graph., 2012

Bounding the Probability of Error for High Precision Optical Character Recognition.
Journal of Machine Learning Research, 2012

2010
Improving state-of-the-art OCR through high-precision document-specific modeling.
Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010


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