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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 7 of 7  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2023-02-22
10:00		 Hokkaido Hokkaido Univ. ITS2022-60 IE2022-77 Unsupervised domain adaptation (UDA) is extremely effective for transferring knowledge from a label-rich source domain t... [more] ITS2022-60 IE2022-77
pp.101-106
PRMU, IPSJ-CVIM 2022-03-10
09:15		 Online Online Unsupervised adaptation of appearance-based gaze estimation models for domains with different label distributions.
Takuru Shimoyama, Yusuke Sugano (The Univ. of Tokyo) PRMU2021-61		 The annotation of gaze estimation is time-consuming, and it is not easy to collect training data under the exact same li... [more] PRMU2021-61
pp.7-12
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-22
10:45		 Online Online ITS2021-46 IE2021-55 There has been a tremendous progress in unsupervised domain adaptation (UDA), which aims to transfer knowledge acquired ... [more] ITS2021-46 IE2021-55
pp.127-132
MI 2022-01-26
15:00		 Online Online [Special Talk] TBA
Ryoma Bise (Kyushu Univ.) MI2021-66		 Supervised learning (e.g., deep learning) has been used for various tasks in biomedical image analysis. While supervised... [more] MI2021-66
p.88
PRMU 2021-12-16
14:45		 Online Online Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images
Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xiang Ruan (tiwaki), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2021-31		 Most of the existing deep learning based logo detection methods typically use a large amount of annotated training data,... [more] PRMU2021-31
pp.43-44
PRMU 2020-12-17
15:10		 Online Online Hierarchical Contrastive Adaptation for Cross-Domain Object Detection
Ziwei Deng, Quan Kong, Naoto Akira, Tomoaki Yoshinaga (Hitachi) PRMU2020-46		 Object detection based on deep learning has been enormously developed in recent years. However, applying detectors train... [more] PRMU2020-46
pp.47-52
NLC 2020-09-10
15:25		 Online Online Unsupervised Domain Adaptation for Dialogue Sequence Labeling -- Application to Contact Center Tasks --
Shota Orihashi, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Ryo Masumura (NTT) NLC2020-8		 This paper presents an unsupervised domain adaptation for utterance-level sequence labeling of conversation in a contact... [more] NLC2020-8
pp.34-39
 Results 1 - 7 of 7  /   
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