Carl Doersch: Projects

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	Video Action Transformer Network Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman in CVPR 2019 [arXiv] A Better Baseline for AVA Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman in CVPR 2018 ActivityNet Workshop [arXiv]
	Kickstarting Deep Reinforcement Learning Simon Schmitt, Jony Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech Czarnecki, Joel Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, Ali Eslami in NIPS 2018 Reinforcement Learning Workshop [arXiv]
	Learning Visual Question Answering by Bootstrapping Hard Attention Mateusz Malinowski, Carl Doersch, Adam Santoro, Peter Battaglia in ECCV 2018 [arXiv] https://arxiv.org/abs/1809.04482 Mateusz Malinowski, Carl Doersch in ECCV 2018 Workshop on Shortcomings in Vision and Language [arXiv]
	Multi-task Self-Supervised Visual Learning Carl Doersch and Andrew Zisserman in ICCV 2017 [arXiv] [Show BibTex] @inproceedings{doersch2017multitask, title = {Multi-task Self-Supervised Visual Learning}, author = {Doersch, Carl and Zisserman, Andrew}, booktitle = {International Conference on Computer Vision}, year = {2017}, }
	Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch. Carnegie Mellon Thesis Dissertation [pdf] [Show BibTex] @phdthesis{doersch2016unsupervised, title = {Supervision Beyond Manual Annotations for Learning Visual Representations}, author = {Doersch, Carl}, school = {Carnegie Mellon University}, year = {2016}, }
	Tutorial on Variational Autoencoders Carl Doersch. Arxiv Tech Report, June 2016 [arXiv] [Show BibTex] @article{doersch2016tutorial, title = {Tutorial on Variational Autoencoders}, author = {Doersch, Carl}, journal = {arXiv preprint arXiv:1606.05908}, year = {2016}, }
	An Uncertain Future: Forecasting from Static Images using Variational Autoencoders Jacob Walker, Carl Doersch, Abhinav Gupta, and Martial Hebert. in ECCV 2016 [webpage] [arXiv] [Show BibTex] @inproceedings{walker2016uncertain, title = {An Uncertain Future: Forecasting from Static Images using Variational Autoencoders}, author = {Walker, Jacob and Doersch, Carl and Gupta, Abhinav and Hebert, Martial}, booktitle = {European Conference on Computer Vision}, year = {2016}, }
	Data-dependent Initializations of Convolutional Neural Networks Philipp Krähenbühl, Carl Doersch, Jeff Donahue, and Trevor Darrell. ICLR, 2016 [arxiv]
	Unsupervised Visual Representation Learning by Context Prediction Carl Doersch, Abhinav Gupta, and Alexei A. Efros. in ICCV 2015 (oral) [webpage] [arXiv] [Show BibTex] @inproceedings{doersch2015unsupervised, title = {Unsupervised Visual Representation Learning by Context Prediction}, author = {Doersch, Carl and Gupta, Abhinav and Efros, Alexei A.}, booktitle = {International Conference on Computer Vision}, year = {2015}, }
	Context as Supervisory Signal: Discovering Objects with Predictable Context Carl Doersch, Abhinav Gupta, and Alexei A. Efros. In ECCV 2014 [Show BibTex] @inproceedings{doersch2014context, title = {Context as Supervisory Signal: Discovering Objects with Predictable Context}, author = {Doersch, Carl and Gupta, Abhinav and Efros, Alexei A.}, booktitle = {European Conference on Computer Vision}, year = {2014}, }
	Mid-Level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, and Alexei A. Efros. In NIPS 2013 [Show BibTex] @inproceedings{doersch2013mid, title = {Mid-Level Visual Element Discovery as Discriminative Mode Seeking}, author = {Doersch, Carl and Gupta, Abhinav and Efros, Alexei A.}, booktitle = {Neural Information Processing Systems (NIPS)}, year = {2013}, }
	What Makes Paris Look like Paris? Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros. In SIGGRAPH 2012 (oral) Republished on the cover of the CACM magazine Dec. 2015 [Show BibTex] @article{doersch2012what, title = {What Makes Paris Look like Paris?}, author = {Carl Doersch and Saurabh Singh and Abhinav Gupta and Josef Sivic and Alexei A. Efros}, journal = {ACM Transactions on Graphics (SIGGRAPH)}, volume = {31}, number = {4}, year = {2012}, } @article{doersch2015makes, title={What makes Paris look like Paris?}, author={Doersch, Carl and Singh, Saurabh and Gupta, Abhinav and Sivic, Josef and Efros, Alexei A}, journal={Communications of the ACM}, volume={58}, number={12}, pages={103--110}, year={2015}, publisher={ACM} }
	Bounding the Probability of Error for High Precision Optical Character Recognition Gary B. Huang, Andrew Kae, Carl Doersch, and Erik Learned-Miller. In JMLR 2012 [pdf] [Show BibTex] @article{huang2012bounding, title={Bounding the Probability of Error for High Precision Optical Character Recognition}, author={Huang, G.B. and Kae, A. and Doersch, C. and Learned-Miller, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={363--387}, year={2012} }
	Improving state-of-the-art OCR through high-precision document-specific modeling. Andrew Kae, Gary B. Huang, Carl Doersch, and Erik Learned-Miller. In CVPR 2010 [pdf] [Show BibTex] @INPROCEEDINGS{kae10improving, author = {Andrew Kae and Gary B. Huang and Carl Doersch and Erik Learned-Miller}, title = {Improving state-of-the-art OCR through high-precision document-specific modeling.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2010}, month = {Jun} }