▶ Date: Nov. 30 16:40~17:25

▶ Speaker: Ming-Hsuan Yang, UC Merced

In the first part of this talk, I will present recent results on semantic-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, ....

Ming-Hsuan Yang 교수님은 University of Illinois at Urbana-Champaign에서 박사학위를 받고, 2008년부터 현재까지 캘리포니아 메르세드대학(University of California, Merced)에서 교수로 재직중입니다. 2019년 IEEE 컴퓨터비전 컨퍼런스 공동의장으로 활동하고 있어 관련 전공 연구자들은 알아두시면 좋을 것 같네요! 

▶Author: Yang Ming-Hsuan, Ahuja Narendra

▶Title: Face detection and gesture recognition for human-computer interaction

▶Publisher: Kluwer Academic,2001.

▶Page: 182 p.

▶ISBN: 9780792374091, 0792374096 

▶Subject: Human-computer interaction, Image processingDigital techniques.

Suha Kwak

Daegu Gyeongbuk Intstitute of Science and Technology (DGIST)

Semantic segmentation is a visual recognition task aiming to estimate pixel-level class labels in images. This problem has been recently handled by Deep Convolutional Neural Networks (DCNNs), and the state of the art based on DCNNs achieve impressive records on public benchmarks. However, learning DCNNs demands a large number of annotated training data while segmentation annotations in existing datasets are significantly limited in terms of both quantity and diversity due to the heavy annotation cost. Weakly supervised approaches tackle this issue by leveraging weak annotations such as bounding boxes and scribbles, which are either readily available in existing large-scale datasets or easily obtained thanks to their low annotation costs. In this talk, I will introduce our recent approaches to weakly supervised semantic segmentation based on image-level class label, which is the form of minimum supervision indicating only presence or absence of a semantic entity in an image. Learning semantic segmentation with image-level class label is a significantly ill-posed problem since neither object location nor shape is informed by the label. We tackled this challenging problem by employing (1) unsupervised techniques revealing low-level image structures, (2) web-crawled videos as additional data sources, and (3) DCNN architectures appropriate for learning segmentation with incomplete pixel-level annotations. I will conclude this talk with a few suggestions for future research directions worth to investigate for further improvement.

Sunghyun Cho

Daegu Gyeongbuk Intstitute of Science and Technology (DGIST)

Dec. 1 09:50~10:20

One goal in computational photography is to overcome limitations of traditional cameras such as blur. Blur, which is one of the most annoying artifacts in photographs, is caused by long exposure time or lens aberration. Deblurring is a problem to remove blur from a blurry image so that valuable hidden information can be revealed. Deblurring can benefit consumer cameras, medical imaging, aerial and satellite imaging, and many others. In this talk, I will briefly introduce some of our past and recent results in image deblurring including convergence analysis of deblurring, and deblurring in low-light environments. Besides deblurring, I will share my ideas on possible future directions in computational photography research.