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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI 2023-11-16
16:50		 Tottori
(Primary: On-site, Secondary: Online)		 On Food Plant Classification from Luehdorfia Japonica Images using Multi-label Classification ABN
Tsubasa Hirakawa, Takaaki Arai, Takayoshi Yamashita, Hironobu Fujiyoshi, Yuichi Oba, Hiromichi Fukui (Chubu Univ.), Masaya Yago (Tokyo Univ.) PRMU2023-27		 Butterfly is a familiar taxon. Because of the abundance of specimens and the ease of comparison between specimens, regio... [more] PRMU2023-27
pp.62-67
IMQ, IE, MVE, CQ
(Joint) [detail]		 2023-03-16
14:25		 Okinawa Okinawaken Seinenkaikan (Naha-shi)
(Primary: On-site, Secondary: Online)		 [Short Paper] Multi-Level Attention Network for Fine-Grained Fashion Retrieval
Ling Xiao, Toshihiko Yamasaki (UTokyo) IMQ2022-60 IE2022-137 MVE2022-90		 This paper proposes an attribute-guided multi-level attention network (AG-MLAN) to learn fine-grained fashion similarity... [more] IMQ2022-60 IE2022-137 MVE2022-90
pp.198-199
PRMU, IPSJ-CVIM 2022-03-10
16:05		 Online Online Learning from AI: Proposal of interactive learning methods for learners learning from DNN embedded expertise knowledge as a teacher
Kohei Hattori, Hironobu Fujiyoshi, Takayoshi Yamashita, Tsubasa Hirakawa (CHUBU) PRMU2021-70		 (To be available after the conference date) [more] PRMU2021-70
pp.60-65
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-22
16:45		 Online Online A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) 		This paper presents a note on accurate distress classification using deep learning considering confidence in attention m... [more]
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2021-02-18
11:35		 Online Online A Note on Accurate Distress Image Classification of Road Structures Using Attention Map based on Text Data
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) 		This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level... [more]
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