[image 02138] 講演会２(11/28)のご案内

[Akihiro Sugimoto]

皆さま

下記の講演会を開催しますので、ふるってご参加ください。
参加登録、参加費などは不要です。なお、先日ご案内差し上げ
ましたが、本講演に引き続き、Tomas Pajdla (チェコ工科大）の
講演がありますので、両方にご参加いただけますと幸いです。

よろしくお願いします。

杉本
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日時:   11月28日(月）　15:30 - 16:30  
場所:   国立情報学研究所　　2005 号室(20F)
　　　（東京都千代田区一ツ橋2-1-2）
　　　　http://www.nii.ac.jp/about/access/　

講演者:   Ming-Hsuan Yang (UC Merced, USA)       
               https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=ja

タイトル:    Recent Results on Image Editing and Learning Filters

アブスト:
In the first part of this talk, I will present recent results on
sematic-aware image editing. Skies are common backgrounds in photos but are
often less interesting due to the time of photographing. Professional
photographers correct this by using sophisticated tools with painstaking
efforts that are beyond the command of ordinary users. In this work, we
propose an automatic background replacement algorithm that can generate
realistic, artifact-free images with diverse styles of skies. The key idea
of our algorithm is to utilize visual semantics to guide the entire process
including sky segmentation, search and replacement. First we train a deep
convolutional neural network for semantic scene parsing, which is used as
visual prior to segment sky regions in a coarse-to-fine manner. Second, in
order to find proper skies for replacement, we propose a data-driven sky
search scheme based on semantic layout of the input image. Finally, to
re-compose the stylized sky with the original foreground naturally, an
appearance transfer method is developed to match statistics locally and
semantically. We show that the proposed algorithm can automatically
generate a set of visually pleasing results. In addition, we demonstrate
the effectiveness of the proposed algorithm with extensive user studies.

In the second part, I will present recent results on learning image filters
for low-level vision. We formulate numerous low-level vision problems
(e.g., edge preserving filtering and denoising) as recursive image
filtering via a hybrid neural network. The network contains several
spatially variant recurrent neural networks (RNN) as equivalents of a group
of distinct recursive filters for each pixel, and a deep convolutional
neural network (CNN) that learns the weights of the RNNs. The deep CNN can
learn regulations of recurrent propagation for various tasks and
effectively guides recurrent propagation over an entire image. The proposed
model does not need large number of convolutional channels nor big kernels
to learn features for low-level vision filters. It is much smaller and
faster compared to a deep CNN based image filter. Experimental results show
that many low-level vision tasks can be effectively learned and carried out
in real-time by the proposed algorithm.


略歴: 
Ming-Hsuan Yang is an associate professor in Electrical Engineering
and Computer Science at University of California, Merced. He received the
PhD degree in Computer Science from the University of Illinois at
Urbana-Champaign in 2000. He serves as an area chair for several
conferences including IEEE Conference on Computer Vision and Pattern
Recognition, IEEE International Conference on Computer Vision, European
Conference on Computer Vision, Asian Conference on Computer Vision, AAAI National
Conference on Artificial Intelligence, and IEEE International Conference on
Automatic Face and Gesture Recognition. He serves as a program co-chair for
IEEE International Conference on Computer Vision in 2019 as well as Asian
Conference on Computer Vision in 2014, and general co-chair for Asian
Conference on Computer Vision in 2016. He serves as an associate editor of
the IEEE Transactions on Pattern Analysis and Machine Intelligence (2007 to
2011), International Journal of Computer Vision, Computer Vision and Image
Understanding, Image and Vision Computing, and Journal of Artificial
Intelligence Research. Yang received the Google faculty award in 2009, and
the Distinguished Early Career Research award from the UC Merced senate in
2011, the Faculty Early Career Development (CAREER) award from the National
Science Foundation in 2012, and the Distinguished Research Award from UC
Merced Senate in 2015.

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