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UID:rifi9kd23ckudgvor04butk44s@google.com
CATEGORIES:RTportal.ru
CREATED:20170606T092329
SUMMARY:семинар CV: Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
LOCATION:ШАД в Яндексе, аудитория Гарвард
DESCRIPTION;ENCODING=QUOTED-PRINTABLE: Александр Колесников из IST(http://pub.ist.ac.at/~akolesnikov/)\n\nTitle: 
 "Seed, Expand and Constrain: Three Principles for Weakly-Supervised\nImage 
 Segmentation"\n\nAbstract: "We introduce a new loss function for the weakly
 -supervised\ntraining of semantic image segmentation models based on three 
 guiding\nprinciples: to seed with weak location cues, to expand objects bas
 ed on\nthe information about which classes can occur, and to constrain the\
 nsegmentations to coincide with image boundaries. We show experimentally\nt
 hat training a deep convolutional neural network using the proposed loss\nf
 unction leads to substantially better segmentations than previous\nstate-of
 -the-art methods on the challenging PASCAL VOC 2012 dataset. We\nfurthermor
 e give insight into the working mechanism of our method by a\ndetailed expe
 rimental study that illustrates how the segmentation quality\nis affected b
 y each term of the proposed loss function as well as their\ncombinations."\
 n\nЗапись видео скорее всего будет.\nСеминары проходят в помещениях ШАД в Я
 ндексе, аудитория Гарвард, начало в 16:30. Приходить можно всем желающим, н
 о если у вас нет пропуска, то нужно заранее написать мне в fb и подойти к т
 орцу здания Тимура Фрунзе 11с5 за 5 минут до начала семинара. Кто планирует
  ходить постоянно напишите Артему, чтобы запустить процесс создания пропуск
 ов (см емейл  This event was imported from: https://rtportal.ru/index.php/k
 alendar-sobytij/eventdetail/131/-/seminar-cv-seed-expand-and-constrain-thre
 e-principles-for-weakly-supervised-image-segmentation?tmpl=component
DTSTAMP:20260812T201258Z
DTSTART;TZID=Europe/Moscow:20160323T163000
DTEND;TZID=Europe/Moscow:20160323T180000
SEQUENCE:0
TRANSP:OPAQUE
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