Yusuke Matsui

https://www.hal.t.u-tokyo.ac.jp/~matsui/index.html

My research interest includes computer vision and multimedia processing. More specifically, I am interested in manga image processing. With the help of computer vision techniques, can we maka the manga-reading experience more enjoyable? Can we enhance the skills of novices and let them enjoy drawing manga? To answer these questions, I have proposed several applications, e.g., manga retargeting, retrieval, and drawing assistance.

Illustration2Vec: A Semantic Vector Representation of Illustrations

http://illustration2vec.net/papers/illustration2vec-main.pdf
http://illustration2vec.net/papers/illustration2vec-supp.pdf
http://illustration2vec.net/

Referring to existing illustrations helps novice drawers to realize their ideas. To find such helpful references from a large image collection, we first build a semantic vector representation of illustrations by training convolutional neural networks. As the proposed vector space correctly reflects the semantic meanings of illustrations, users can efficiently search for references with similar attributes. Besides the search with a single query, a semantic morphing algorithm that searches the intermediate illustrations that gradually connect two queries is proposed. Several experiments were conducted to demonstrate the effectiveness of our methods.

Andrej Karpathy Academic Website

http://cs.stanford.edu/people/karpathy/

Andrej Karpathy
Stanford Computer Science Ph.D. student

CS231n Convolutional Neural Networks for Visual Recognition

http://cs231n.github.io/

These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition.

Stanford University CS231n: Convolutional Neural Networks for Visual Recognition

http://vision.stanford.edu/teaching/cs231n/

Object detection - Pierre Sermanet (Google)

https://docs.google.com/presentation/d/1EukM17bSCDqmxktFS2Yksi6gLwL0XSW0z578rpG08a0/edit?usp=sharing

Object Detection with Deep Learning
CVPR 2014 Tutorial
Pierre Sermanet, Google Research

Deep Learning for Computer Vision

https://sites.google.com/site/deeplearningcvpr2014/

TUTORIAL ON DEEP LEARNING FOR VISION
A tutorial in conjunction with the Intl. Conference in Computer Vision (CVPR) 2014.

CIFAR-10 and CIFAR-100 datasets

http://www.cs.toronto.edu/~kriz/cifar.html

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.

CIFAR-10でstate of the artのスコアが出せる、インターネットに落ちている中で最強のコード - デー

http://ultraist.hatenablog.com/entry/2014/08/23/025614

CIFAR-10のstate of the artである0.912を微妙に超える精度(0.9173)が出せるようになったのでソースコードを公開します。