[image 01682] 講演会のお知らせ: 牧 淳人先生 (KTH, Sweden) 3月28日

[Shin'ichi Satoh]

image-mlの皆様、

国立情報学研究所の佐藤真一です。KTHの牧 淳人先生による以下の講演会のお
知らせをお送りいたします。参加無料、参加登録も不要です。どうぞご参加下
さい。

-- Shin'ichi


日時: 3月28日(月) 16:00-17:00
場所: 国立情報学研究所 20階 セミナー室(1)
http://www.nii.ac.jp/access/

Title: On the Utility of Generic ConvNets Visual Representations

Speaker:
Dr. Atsuto Maki
Computer Vision and Active Perception Lab (CVAP),
Royal Institute of Technology (KTH), Sweden

Abstract:
We consider the utility of global image descriptors given by Deep
Convolutional Networks (ConvNets) for visual recognition tasks.  Given
a ConvNet which has been trained with a large labeled data set, the
feed-forward units activation at a certain layer can be used as a
generic representation of a new input image for a target task. We will
highlight three aspects of this common scenario in the context of
transfer learning.  We will first visit several factors affecting the
transferability, including those for learning such as network design
and distribution of training data as well as post-learning factors
such as layer choice of the trained ConvNet. By optimising these
factors, we see that significant improvements can be achieved on
various standard visual recognition tasks. Then, we will explore what
information resides in such representations; interestingly we find
strong spatial information implicit, which was unexpected in a network
trained for classification problems. We will finally introduce an
efficient pipeline in an application to visual instance retrieval
where spatial search is enabled by ConvNet representations. Work
presented was performed with the computer vision group of CVAP.

Short Bio:
Atsuto Maki is a Docent at the Royal Institute of Technology (KTH),
Sweden. He received the BE degree from Kyoto University, the ME degree
from the University of Tokyo, and the PhD degree from KTH (in
1996). After serving as a researcher at Toshiba Corporate R&D Center,
a senior researcher at Toshiba Research Cambridge, U.K., and an
associate professor at Kyoto University, he moved to KTH in 2013. His
research interests cover a broad range of topics in machine learning
and computer vision, including motion and object recognition,
clustering, subspace analysis, stereopsis, and representation
learning. He is currently a board member of Swedish Society for
Automated Image Analysis (SSBA).