[image 03488] 再送: 講演会(4/17)のご案内

Akihiro Sugimoto sugimoto @ nii.ac.jp
2019年 4月 17日 (水) 14:42:49 JST




On Fri, 12 Apr 2019 09:23:19 +0900
Akihiro Sugimoto <sugimoto @ nii.ac.jp> wrote:

> 皆様
> 下記の講演会を開催しますので、お時間のある方はご参加ください。
> 事前登録、参加費不要です。
> 杉本
> -----------------------
> 日時:   4/17(水) 17:00-18:00
> 場所:   国立情報学研究所 19F 1901号室
>            (東京都千代田区一ツ橋)
>             https://www.nii.ac.jp/about/access/
> 講演者: Professor Ming-Hsuan Yang 
>               (UC Merced and a Senior Staff Research Scientist at Google)
>               http://faculty.ucmerced.edu/mhyang
> タイトル: Semantic Segmentation via Domain Adaption and Adversarial Learning
> 概要: 
> Convolutional neural network-based approaches for semantic
> segmentation rely on supervision with pixel-level ground truth, but may not
> generalize well to unseen image domains. As the labeling process is tedious
> and labor intensive, developing algorithms that can adapt source ground
> truth labels to the target domain is of great interest. In the first part
> of this talk, I will present an adversarial learning method for domain
> adaptation in the context of semantic segmentation. Considering semantic
> segmentations as structured outputs that contain spatial similarities
> between the source and target domains, we adopt adversarial learning in the
> output space. To further enhance the adapted model, we construct a
> multi-level adversarial network to effectively perform output space domain
> adaptation at different feature levels. Extensive experiments and ablation
> study are conducted under various domain adaptation settings, including
> synthetic-to-real and cross-city scenarios. We show that the proposed
> method performs favorably against the state-of-the-art methods in terms of
> accuracy and visual quality. In the second part of this talk, I will
> discuss a method for semi-supervised semantic segmentation using an
> adversarial network. While most existing discriminators are trained to
> classify input images as real or fake on the image level, we design a
> discriminator in a fully convolutional manner to differentiate the
> predicted probability maps from the ground truth segmentation distribution
> with the consideration of the spatial resolution. We show that the proposed
> discriminator can be used to improve semantic segmentation accuracy by
> coupling the adversarial loss with the standard cross entropy loss of the
> proposed model. In addition, the fully convolutional discriminator enables
> semi-supervised learning through discovering the trustworthy regions in
> predicted results of unlabeled images, thereby providing additional
> supervisory signals. In contrast to existing methods that utilize
> weakly-labeled images, our method leverages unlabeled images to enhance the
> segmentation model. Experimental results on the PASCAL VOC 2012 and
> Cityscapes datasets demonstrate the effectiveness of the proposed algorithm.
> Bio: Ming-Hsuan Yang <http://faculty.ucmerced.edu/mhyang> a Professor in
> Electrical Engineering and Computer Science at University of California at
> Merced and a Senior Staff Research Scientist at Google. After receiving his
> PhD degree in Computer Science from University of Illinois at
> Urbana-Champaign, he worked at Honda Research Institute before joining UC
> Merced in 2008. Yang received the Google Faculty Research 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. He serves as an area chair for several
> conferences including IEEE Conference on IEEE International Conference on
> Computer Vision (ICCV),  IEEE Computer Vision and Pattern Recognition
> (CVPR), European Conference on Computer Vision (ECCV), and Asian Conference
> on Computer (ACCV). Yang serves as a program co-chair for ICCV in 2019 as
> well as ACCV in 2014, and general co-chair for ACCV in 2016. He serves as
> an associate editor of the IEEE Transactions on Pattern Analysis and
> Machine Intelligence, International Journal of Computer Vision, Computer
> Vision and Image Understanding, Image and Vision Computing, and Journal of
> Artificial Intelligence Research. In 2018, he was selected as one of the
> Highly Cited Researchers by Clarative Analytics (formerly Thomson Reuters).
> Yang received paper awards from UIST 2017, ACCV 2018 and CVPR 2018. He is
> an IEEE Fellow.
> --------------------
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