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projetos:printcap [2021/11/05 14:50] karine [2021] |
projetos:printcap [2021/11/05 14:54] karine [Fevereiro - 2020] |
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| ====== 2020 ====== | ====== 2020 ====== | ||
| - | - Researcher: | + | - Researcher or Student: |
| * Type of event: | * Type of event: | ||
| * Event: | * Event: | ||
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| - | ====== Visitas e palestras de estrangeiros 2020 ====== | + | ====== Visitas e palestras de estrangeiros - 2019 a 2021 ====== |
| - | ====== Março - 2020 ====== | + | ====== 2020 ====== |
| - Researcher: Dr. Nathan Jacobs | - Researcher: Dr. Nathan Jacobs | ||
| - | == Mini-bio do Pesquisador: == | ||
| - | Dr. Nathan Jacobs earned a B.S. in Computer Science at the University of Missouri (1999) and a Ph.D. in Computer Science at Washington University in St. Louis (2010). Since then, he has been a Professor of Computer Science at the University of Kentucky, USA. He is the recipient of a National Science Foundation CAREER award in 2016 for his work at the intersection of computer vision, video surveillance, and remote sensing. During 2017-2018 he was a Visiting Research Scientist at Orbital Insight, Inc. where he developed methods for building detection and classification from satellite imagery. Dr. Jacobs' research area is computer vision; his specialty is developing learning-based algorithms and systems for processing large-scale image collections. His current focus is on developing techniques for understanding the visual world from geotagged imagery, including images from social networks, publicly available outdoor webcams, and satellites. His research has been funded by the United States National Science Foundation (NSF), National Institutes of Health (NIH), Defense Advanced Research Projects Agency (DARPA), Intelligence Advanced Research Projects Activity (IARPA), National Geospatial-Intelligence Agency (NGA), Army Research Laboratory (ARL), Air Force Research Laboratory (AFRL), and Google. | ||
| - | |||
| - | == Description: == | ||
| - | Over the past ten years, deep convolutional neural networks have emerged as an essential building block for the creation of state-of-the-art computer vision systems. While research challenges remain, today they can be easily incorporated into systems for a wide range of applications. They possess a unique combination of benefits that make this possible: they can be trained in an end-to-end manner, such that all parameters are optimized for the task; they are fast at inference time, without requiring sampling or other expensive inference steps; and the existing software tools enable the flexible integration of a wide variety of data types, often with very little custom code. This two-part course will cover foundations, both theoretical and practical, as well as the current state-of-the-art in computer vision research. | ||
| - | * Other infos (links, documents, fotos, etc.): | ||
| - | |||
| * Date: **12, 13 e 14 de março de 2020**. | * Date: **12, 13 e 14 de março de 2020**. | ||
| * Type of event: Curso | * Type of event: Curso | ||
| * Title: Deep Convolutional Neural Networks: Foundations to Frontiers | * Title: Deep Convolutional Neural Networks: Foundations to Frontiers | ||
| - | |||
| * Date: **10 de março de 2020**. | * Date: **10 de março de 2020**. | ||
| * Type of event: Seminário | * Type of event: Seminário | ||
| * Title: What, Where, and When: Mapping the World Using Webcams, Cell Phones, and Satellites | * Title: What, Where, and When: Mapping the World Using Webcams, Cell Phones, and Satellites | ||
| - | * Abstract: | + | |
| - | Trillions of pixels are collected every day. Together they provide many perspectives of the world, ranging from what someone had for dinner to where new buildings have been constructed. The key challenge is translating this raw data into useful information in a way that scales to large geographic areas. This talk provides an overview of my work in developing algorithms that can learn how to extract such information with limited human effort. Topics will include extracting phenological trends from outdoor webcams, combining social media and satellite data to map visual attributes, and learning to predict population distributions. | + | |
| - | ====== Fevereiro - 2020 ====== | ||
| - | - Researcher: Jordan | ||
| - | * Mini-bio do Pesquisador: | ||
| - | * Description: | ||
| - | * Date: 17 de Fevereiro de 2020 | ||
| - | * Type of event: Seminário | ||
| - | * Other infos (links, documents, fotos, etc.): | ||
| ====== Janeiro - 2020 ====== | ====== Janeiro - 2020 ====== | ||
