Online edition: ISSN 2432-6380
[TOP] | [2017] | [2018] | [2019] | [2020] | [2021] | [2022] | [2023] | [Japanese] / [English]
PRMU2020-38
Inter-intra Contrastive Framework for Self-supervised Spatio-temporal Learning
Li Tao, Xueting Wang, Toshihiko Yamasaki (UTokyo)
pp. 1 - 6
PRMU2020-39
Synthesize talking anime-heads images by tunneling through human-heads domain
Shun Fujiuchi, Ryo Hachiuma, Kunihiro Hasegawa, Hideo Saito (Keio Univ.)
pp. 7 - 11
PRMU2020-40
Simultaneous learning of object foreground, pose and class using only class teacher
Shunsuke Yoneda (Tottori Univ.), Go Irie (NTT), Masashi Nisiyama, Yoshio Iwai (Tottori Univ.)
pp. 12 - 17
PRMU2020-41
Simultaneous pose-region estimation for people tracking
Kazuhiko Watanabe, Toshikazu Wada (Wakayama Univ.)
pp. 18 - 23
PRMU2020-42
Fast algorithm for low-rank tensor completion in multi-way delay embedded space
Ryuki Yamamoto, Tatsuya Yokota (Nagoya Institute of Tech.), Akira Imakura (Univ. of Tsukuba), Hidekata Hontani (Nagoya Institute of Tech.)
pp. 24 - 29
PRMU2020-43
A Novel Data Augmentation Framework Based on SeqGAN for Sentiment Analysis
Jiawei Luo, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.)
pp. 30 - 35
PRMU2020-44
Belonging Network
-- Few-shot One-class Image Classification for Classes with Various Distributions --
Takumi Ohkuma, Hideki Nakayama (UT)
pp. 36 - 41
PRMU2020-45
Improving the accuracy of unsupervised segmentation by introducing a Laplacian filter loss function
-- Application to automotive wire harness components --
Yuki Matsumoto (SEI)
pp. 42 - 46
PRMU2020-46
Hierarchical Contrastive Adaptation for Cross-Domain Object Detection
Ziwei Deng, Quan Kong, Naoto Akira, Tomoaki Yoshinaga (Hitachi)
pp. 47 - 52
PRMU2020-47
Visual inspection system with a small number of anomalous data using DevNet
Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST)
pp. 53 - 57
PRMU2020-48
[Short Paper]
Few-Shot Incremental Learning by Unifying with Variational Autoencoder
Keita Takayama, Kuniaki Uto, Koichi Shinoda (TokyoTech)
pp. 58 - 62
PRMU2020-49
Towards Discovery of Relevant Latent Factors with Limited Data
Mohit Chhabra, Quan Kong, Tomoaki Yoshinaga (Hitachi)
pp. 63 - 68
PRMU2020-50
Vehicle detection using visualization of deep learning from in-vehicle night-time camera image
Tatsuya Oyabu, Gosuke Ohashi (Shizuoka Univ.)
pp. 69 - 74
PRMU2020-51
Learning Method for Ambiguous Lesion Boundaries in Endoscopy Images
Yuta Kochi (Univ. of Tsukuba/AIST), Hirokazu Nosato (AIST), Atsushi Ikeda (Univ. of Tsukuba Hosp.), Hidenori Sakanashi (AIST)
pp. 75 - 79
PRMU2020-52
Transfer learning from sparse models
-- Two approaches and optimization issues --
Tomoya Sakai, Rabi Yamada, Ryoji Ishibashi, Hiroyuki Takada (Nagasaki Univ.)
pp. 80 - 85
PRMU2020-53
Report on MIRU 2020 Young Researchers Program
Yuzuko Utsumi (OPU), Takafumi Iwaguchi (Kyushu Univ.), Xueting Wang (Univ. of Tokyo), Masanori Suganuma (Tohoku Univ./RIKEN), Mai Nishimura (Kyoto Univ./OSX), Kensho Hara (AIST), Tsubasa Hirakawa (Chubu Univ.), Hiroshi Fukui (NEC)
pp. 86 - 92
PRMU2020-54
Corner point detection with reliability metric for homography warping of planer objects
Hiroya Fujiura, Toshikazu Wada (Wakayama Univ)
pp. 93 - 98
PRMU2020-55
Pear Flower Cluster Detection Method Using Deep Learning and Branch Extraction
Shunsuke Aoki, Tatsuya Yamazaki (Niigata Univ.)
pp. 99 - 104
PRMU2020-56
CNN and 2D BLSTM for Local Feature Extraction in Handwritten Mathematical Expression Recognition
Kei Morizumi, Cuong Tuan Nguyen, Ikuko Shimizu, Masaki Nakagawa (TUAT)
pp. 105 - 110
PRMU2020-57
Depth image prediction of transparent objects based on image-to-image translation
Ryo Iimori (KIT), Ryosuke Kubota, Kiyoshi Kogure (KIT)
pp. 111 - 115
PRMU2020-58
Supervised disentangled representation learning
-- Disentangling features using classifier --
Shujiro Kuroda, Toshikazu Wada (Wakayama Univ.)
pp. 116 - 121
PRMU2020-59
Zero-shot generative model considering attribute uncertainty
Yuta Sakai (Waseda Univ.), Kenta Mikawa (SIT), Masayuki Goto (Waseda Univ.)
pp. 122 - 127
PRMU2020-60
Construction of SSD model applied Feature Contraction and Rand Augment by small training data
Tomokazu Ozawa (UNICO), Yuki Matsumoto, Katsushi Miura (SEI), Takuya Okuno (SCE)
pp. 128 - 132
PRMU2020-61
Regularization Using Knowledge Distillation in Learning Small Datasets
Ryota Higashi, Toshikazu Wada (Wakayama Univ.)
pp. 133 - 138
PRMU2020-62
A Hybrid Sampling Strategy for Improving the Accuracy of Image Classification with less Data
Ruiyun Zhu, Fumihiko Ino (Osaka Univ.)
pp. 139 - 144
PRMU2020-63
Multi-Task Attention Learning for Fine-grained Recognition
Dichao Liu (NU), Yu Wang (Rits), Kenji Mase (NU), Jien Kato (Rits)
pp. 145 - 150
PRMU2020-64
[Short Paper]
Case Discrimination: Self-supervised Learning for classification of Medical Image
Haohua Dong, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin (Zhejiang Univ.), Hongjie Hu, Xiujun Cai (Sir Run Run Shaw Hospital), Yen-Wei Chen (Ritsumeikan Univ.)
pp. 151 - 155
PRMU2020-65
Estimating 3D regions for grasping an object
Atsuki Tsukamoto, Kiyoshi Kogure (KIT)
pp. 156 - 160
PRMU2020-66
Shohei Kubota, Hideaki Hayashi (Kyushu Univ.), Tomohiro Hayase (Fujitsu Lab.), Seiichi Uchida (Kyushu Univ.)
pp. 161 - 165
PRMU2020-67
An evaluation method of area detection AI based on contribution pattern variation with noise addition
Yasuhide Mori, Naofumi Hama, Masashi Egi (Hitachi)
pp. 166 - 171
PRMU2020-68
Rethinking the local similarity in content-based image retrieval
Longjiao Zhao (Nagoya Univ.), Yu Wang (Ritsumeikan Univ), Yoshiharu Ishikawa (Nagoya Univ.), Jien Kato (Ritsumeikan Univ)
pp. 172 - 176
Note: Each article is a technical report without peer review, and its polished version will be published elsewhere.