BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IEEE Toronto Section - ECPv6.15.17//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IEEE Toronto Section
X-ORIGINAL-URL:https://www.ieeetoronto.ca
X-WR-CALDESC:Events for IEEE Toronto Section
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Toronto
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20201007T140000
DTEND;TZID=America/Toronto:20201007T150000
DTSTAMP:20260702T215429
CREATED:20210430T023717Z
LAST-MODIFIED:20210501T000701Z
UID:10000211-1602079200-1602082800@www.ieeetoronto.ca
SUMMARY:GPT-3 for Vision
DESCRIPTION:On Wednesday\, October 7\, 2020 at 2:00 p.m.\, Dr. Ehsan Kamalinejad will present “GPT-3 for Vision”. \nDay & Time: Wednesday\, October 7\, 2020\n2:00 p.m. – 3:00 p.m. \nSpeaker: Ehsan Kamalinejad\, PhD\nCo-Founder & CTO at Visual One\nAssociate Professor at Cal State East Bay University\nFormer Senior Machine Learning Scientist at Apple\nSan Francisco\, USA \nOrganizer: IEEE Toronto Signal Processing Chapter \nLocation: Virtual – Click here for the Google Meets link. \nContact: Mehrnaz Shokrollahi \nAbstract: Deep learning in computer vision (CV) has proved to be very effective in solving many problems in real world. However\, while the raw number of researches done in standard CV problems (such as ImageNet\nobject classification/detection) has exploded\, the measurable progress in these fields has slowed down. Additionally\, there are many real-world problems in vision that are simply not compatible with the current approaches. This demands a new wave of problem statements in CV (and a new set of benchmarks). This talk focuses on one important set of such problem statements. We propose that many real-world problems in vision are “event recognition” problems. We introduce a concrete definition for the event recognition problem. We will see that this definition of event detection prohibits large sample sets. Hence\, these events need to be recognize based on very few samples. We start by reviewing the current literature and we propose some promising directions for approaching this problem. At the end we show some demos from our recent effort on wrestling with this very challenging problem. Our solution can be best described by the “vision counterpart of GPT-3 few shot learner”. \nRegister: Please check back soon for the registration link. \nBiography: Ehsan Kamalinejad (EK) is a senior machine learning engineer. He is currently working on Visual One which is a YCombinator backed startup he co-founded. Before that he was working for several years at\nApple and Amazon as a staff machine learning engineer. Ehsan holds a faculty position as an associate professor at Cal State East Bay University. He got his PhD from University of Toronto. He has more than 7 years of experience delivering machine learning products in computer vision and natural language processing. His current project\, Visual One\, is about bringing next level intelligence to surveillance cameras.
URL:https://www.ieeetoronto.ca/event/gpt-3-for-vision/
CATEGORIES:Signal Processing
END:VEVENT
END:VCALENDAR