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BEGIN:VEVENT
UID:3ddf46346dcc46b5dbfeb2313c411705
CATEGORIES:PhD Defense
CREATED:20240415T111719
SUMMARY:Maximize Utilization of Support-Set for Few-shot segmentation
DESCRIPTION:<p><strong>Speaker:</strong>&nbsp;Seonghyeon Moon</p><p><strong>Location:</
 strong>&nbsp;CBIM 22 Multipurpose Room</p><p><strong>Committee Composition:
 </strong></p><ul><li>Professor Mubbasir Kapadia from Rutgers University</li
 ><li>Professor Vladimir Pavlovic from Rutgers University</li><li>Professor 
 Mridul Aanjaneya from Rutgers University</li><li>Professor Xia Haifeng from
  Southeast University (external)</li></ul><p><strong>Abstract:</strong></p>
 <p>In Few-shot segmentation(FSS), the support set plays a critical role as 
 it provides target information to segment the target object in a given imag
 e. However, previous works focused on improving network architecture to get
  performance improvements neglecting the importance of how to utilize targe
 t features from the support set. We observed there were performance bottlen
 ecks because of the limited utilization of the support set. We investigated
  information loss and proposed new approaches to retrieve this loss to get 
 more meaningful information from the support set. We validated the effectiv
 eness of our approach by instantiating it into three recent and strong FSS 
 methods. Experimental results on several publicly available FSS benchmarks 
 show that our proposed methods consistently improve performance by visible 
 margins and lead to faster convergence.</p><p><strong>Zoom:</strong></p><p>
 <a href="https://rutgers.zoom.us/j/6756059091?pwd=M0RoNjY4YWN1OWptNys5WlRqM
 Fhmdz09&amp;omn=91204206398" target="_blank" rel="noopener">https://rutgers
 .zoom.us/j/6756059091?pwd=M0RoNjY4YWN1OWptNys5WlRqMFhmdz09&amp;omn=91204206
 398</a><br />Join by SIP<br /><!-- This email address is being protected fr
 om spambots. --><a href="javascript:/* This email address is being protecte
 d from spambots.*/" target="_blank"><span class="cloaked_email ep_b3831f7f"
 ><span data-ep-a="&#54;&#55;&#53;&#54;" data-ep-b="&#111;&#109;"><span data
 -ep-a="&#48;&#53;&#57;&#48;" data-ep-b="rc.c"><span data-ep-a="91&#64;z" da
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 #57;0"><span data-ep-b="&#111;&#111;mc" data-ep-a="&#57;&#49;&#64;&#122;"><
 /span></span></span></span></a><script>RegularLabs.EmailProtector.unCloak("
 ep_2f755073", true);</script><br />Meeting ID: 675 605 9091<br />Password: 
 077449</p>
X-ALT-DESC;FMTTYPE=text/html:<p><strong>Speaker:</strong>&nbsp;Seonghyeon Moon</p><p><strong>Location:</
 strong>&nbsp;CBIM 22 Multipurpose Room</p><p><strong>Committee Composition:
 </strong></p><ul><li>Professor Mubbasir Kapadia from Rutgers University</li
 ><li>Professor Vladimir Pavlovic from Rutgers University</li><li>Professor 
 Mridul Aanjaneya from Rutgers University</li><li>Professor Xia Haifeng from
  Southeast University (external)</li></ul><p><strong>Abstract:</strong></p>
 <p>In Few-shot segmentation(FSS), the support set plays a critical role as 
 it provides target information to segment the target object in a given imag
 e. However, previous works focused on improving network architecture to get
  performance improvements neglecting the importance of how to utilize targe
 t features from the support set. We observed there were performance bottlen
 ecks because of the limited utilization of the support set. We investigated
  information loss and proposed new approaches to retrieve this loss to get 
 more meaningful information from the support set. We validated the effectiv
 eness of our approach by instantiating it into three recent and strong FSS 
 methods. Experimental results on several publicly available FSS benchmarks 
 show that our proposed methods consistently improve performance by visible 
 margins and lead to faster convergence.</p><p><strong>Zoom:</strong></p><p>
 <a href="https://rutgers.zoom.us/j/6756059091?pwd=M0RoNjY4YWN1OWptNys5WlRqM
 Fhmdz09&amp;omn=91204206398" target="_blank" rel="noopener">https://rutgers
 .zoom.us/j/6756059091?pwd=M0RoNjY4YWN1OWptNys5WlRqMFhmdz09&amp;omn=91204206
 398</a><br />Join by SIP<br /><!-- This email address is being protected fr
 om spambots. --><a href="https://www.cs.rutgers.edu/javascript:/* This emai
 l address is being protected from spambots.*/" target="_blank"><span class=
 "cloaked_email ep_b3831f7f"><span data-ep-a="&#54;&#55;&#53;&#54;" data-ep-
 b="&#111;&#109;"><span data-ep-a="&#48;&#53;&#57;&#48;" data-ep-b="rc.c"><s
 pan data-ep-a="91&#64;z" data-ep-b="&#111;&#111;&#109;c"></span></span></sp
 an></span><script>RegularLabs.EmailProtector.unCloak("ep_b3831f7f");</scrip
 t><span class="cloaked_email ep_2f755073" style="display:none;"><span data-
 ep-a="&#54;7&#53;&#54;" data-ep-b="&#111;&#109;"><span data-ep-b="r&#99;&#4
 6;&#99;" data-ep-a="0&#53;&#57;0"><span data-ep-b="&#111;&#111;mc" data-ep-
 a="&#57;&#49;&#64;&#122;"></span></span></span></span></a><script>RegularLa
 bs.EmailProtector.unCloak("ep_2f755073", true);</script><br />Meeting ID: 6
 75 605 9091<br />Password: 077449</p>
DTSTAMP:20260826T211822
DTSTART;TZID=America/New_York:20240501T150000
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