Fetching the paper…

Reading the bibliography…

2016

ResearchDoom and CocoDoom: Learning Computer Vision with Games

Mahendran, A., Bilen, H., Henriques, J. F. et al.

Original
PDF
Post on X

Understand

In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game.

We also introduce the CocoDoom dataset, a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format.
ResearchDoom and CocoDoom can be used to train and evaluate a variety of computer vision methods such as object recognition, detection and segmentation at the level of instances and categories, tracking, ego-motion estimation, monocular depth estimation and scene segmentation.
The code and data are available at http://www.robots.ox.ac.uk/~vgg/research/researchdoom.

ResearchDoom and CocoDoom: Learning Computer Vision with Games · Around

Built on

Playing atari with deep reinforcement learning
Original
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
Earlier work this paper cites.
Multi-digit number recognition from street view imagery using deep convolutional neural networks
Ian J Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, and Vinay Shet · 2014
Earlier work this paper cites.
Synthetic data and artificial neural networks for natural scene text recognition
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.

Similar

Microsoft COCO: common objects in context
Original
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Cited alongside, same era.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Cited alongside, same era.
Openai gym
Original
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.

Then

Synthetic data for text localisation in natural images
Ankush Gupta, Andrea Vedaldi, and Andrew Zisserman · 2016
Closest in time.
Faces in places: Compound query retrieval
Y. Hong, R. Arandjelović, and A. Zisserman · 2016
Closest in time.
Unrealcv: Connecting computer vision to unreal engine
Original
Weichao Qiu and Alan Yuille · 2016
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…