É com prazer que convidamos a tod@s para mais um seminário de
eScience. A palestra será dada pelo Dr. Henrique Morimitsu, ex-aluno
do nosso programa e que acaba de voltar de um posdoc no INRIA com a
Dra. Cordélia Schmid.
Título: Video Object Tracking with Deep Siamese Networks
Palestrante: Henrique Morimitsu
Local: Auditório Jacy Monteiro
Resumo: Video object tracking consists in following an object of
interest in a video sequence. Visual tracking is a challenging task
because the only available information is contained in a single
annotated frame. Recently, deep siamese network architectures have
been proposed to tackle the tracking problem, and they have achieved
high accuracy and speed in standard benchmarks. This talk will provide
an overview of the concept and motivations behind siamese networks and
how recent methods have applied them to video object tracking.
A apresentação será em português.
Nesta sexta-feira, 29/6, teremos a palestra do Dr. Zhangyang (Atlas)
Wang, que trabalha com visão computacional, multimedia e aprendizado
Título: Towards More Robust Outdoor Compute Vision: A Case Study on Haze
Abstract: While many sophisticated models are developed for visual
information processing, very few pay attention to their usability in
the presence of data quality degradations. Most successful models are
trained and evaluated on high quality visual datasets. On the other
hand, the robustness of those computer vision models are often not
assured in degraded visual environments. Especially for outdoor
scenarios. low target resolution, occlusion, motion blur, missing
data, poor light, and bad weather conditions, are all ubiquitous for
visual recognition in the wild. In this talk, I will use haze as a
case example, to introduce our progress on handling non-standard
outdoor visual degradations, using deep learning methods. I will go
through first single image dehazing, follow by video dehazing, then
coming to introduce our recent benchmarking and evaluation efforts.
Local: Sala 144 bloco B - IME - USP
Dr. Zhangyang (Atlas) Wang is an Assistant Professor of Computer
Science and Engineering (CSE), at the Texas A&M University (TAMU).
During 2012-2016, he was a Ph.D. student in the Electrical and
Computer Engineering (ECE) Department, at the University of Illinois
at Urbana-Champaign (UIUC), working with Professor Thomas S. Huang.
Prior to that, he obtained the B.E. degree at the University of
Science and Technology of China (USTC), in 2012. He was a former
research intern with Microsoft Research (summer 2015), Adobe Research
(summer 2014), and US Army Research Lab (summer 2013). Dr. Wang’s
research has been addressing machine learning, computer vision and
multimedia signal processing problems, as well as their
interdisciplinary applications, using advanced feature learning and
optimization techniques. He has co-authored over 40 papers, and
published several books and chapters. He has been granted 3 patents,
and has received around 20 research awards and scholarships. He served
as a guest editor for IEEE TNNLS and EURASIP JASP/JBSB; an Area Chair
for WACV 2019 and ICIP 2017; a TPC co-chair for ICCV AMFG 2017; a
special session co-chair for VCIP 2017; a tutorial organizer/speaker
in SIAM IS 2018, CVPR 2017 and ECCV 2016; a workshop organizer in IEEE
FG, IJCAI and SDM; and a regular reviewer or TPC member for over 40
top journals and conferences. His research has been covered by
worldwide media, such as BBC, Fortune, International Business Times,
TAMU news, and UIUC news & alumni magazine. More could be found at:
Modern science is interdisciplinary and data-intensive. For instance, in the 1000 Genomes Project (www.1000genomes.org), the comparative study of 629 individuals has already generated 7.3 TB of data. Analogous situations exist in fields such as astronomy, agriculture, social sciences, etc. Ten years ago, the problem was how to obtain data. Today, the bottleneck is the need for new computational strategies and tools so that scientists can manage these massive volumes of heterogeneous, distributed, data, so that they can generate new knowledge from the processing, analysis and visualization of the data. This launched the basis of the so-called eScience: the combination of advanced research in computer science and mathematical modeling to allow and accelerate research in other knowledge domains.