BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
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;VALUE=DATE:20201015
DTEND;VALUE=DATE:20201231
DTSTAMP:20260423T082333
CREATED:20201015T142444Z
LAST-MODIFIED:20201015T142444Z
UID:22804-1602720000-1609372799@coe.northeastern.edu
SUMMARY:Meet Your Graduate Student Ambassadors!
DESCRIPTION:Meet your Student Ambassadors! Prospective and Admitted Graduate Students are invited to meet their Student Ambassador via Unibuddy.
URL:https://coe.northeastern.edu/event/meet-your-graduate-student-ambassadors/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201027T150000
DTEND;TZID=America/New_York:20201027T160000
DTSTAMP:20260423T082333
CREATED:20201021T142911Z
LAST-MODIFIED:20201021T142911Z
UID:22830-1603810800-1603814400@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Kunpeng Li
DESCRIPTION:PhD Proposal Review: Attention Mechanism in Deep Learning for Visual Recognition  \nKunpeng Li \nLocation: Zoom Link \nAbstract: Deep learning models have achieved great success in various tasks for visual recognition such as image classification\, semantic segmentation\, visual semantic matching etc. Instead of just treating them as black boxes\, recently\, a tremendous of efforts have been put into the explanations of how these models work and bridging the gap between deep neural networks and human cognition systems. Visual attention is one of the efficient ways to explain the network’s decision by highlighting the regions of images that are responsible for it. It is inspired by the attention mechanism of the human vision system to selectively focus on the salient features in a visual scene. \nThis thesis is on the visual attention in deep learning for visual recognition. For the first time\, we make gradient-based attention maps a natural and explicit component in the training pipeline\, such that they are end-to-end trainable. Then\, we can provide guidance on the attention maps and guide the network to focus on correct things when learning concepts. Under mild assumptions\, our method can be understood as a plug-in to existing convolutional neural networks to improve their generalization performance. Besides\, the improved attention maps also help to provide better localization cues for weakly-supervised semantic segmentation task. \nMoving a step toward higher-level visual understanding with natural language\, we study the effectives of building visual reasoning models on top of the bottom-up attention regions\, so that the learnt visual representations can better capture semantic concepts as in its corresponding text caption. Specifically\, we first build up connections between attention regions and perform reasoning with Graph Convolutional Networks to generate region features with semantic relationships. Then\, we propose to use the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced region features\, select the discriminative information and gradually generate the representation for the whole scene. Evaluations have been conducted on MS-COCO and Flickr30K datasets for the image-text matching task.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-kunpeng-li/
END:VEVENT
END:VCALENDAR