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ORCIDRyosuke Tanno
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2010 – 2019
- 2019
[c12]Keisuke Kiritoshi, Ryosuke Tanno, Tomonori Izumitani:
L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps. CVPR Workshops 2019: 118-121- [c11]Takumi Ege, Yoshikazu Ando, Ryosuke Tanno, Wataru Shimoda, Keiji Yanai
:
Image-Based Estimation of Real Food Size for Accurate Food Calorie Estimation. MIPR 2019: 274-279 - 2018
- [c10]Shu Naritomi, Ryosuke Tanno, Takumi Ege, Keiji Yanai:
FoodChangeLens: CNN-Based Food Transformation on HoloLens. AIVR 2018: 197-199 - [c9]Daichi Horita, Ryosuke Tanno, Wataru Shimoda, Keiji Yanai:
Food category transfer with conditional cycleGAN and a large-scale food image dataset. MADiMa@IJCAI 2018: 67-70 - [c8]Ryosuke Tanno, Daichi Horita, Wataru Shimoda, Keiji Yanai:
Magical Rice Bowl: A Real-time Food Category Changer. ACM Multimedia 2018: 1244-1246 - [c7]Ryosuke Tanno, Takumi Ege, Keiji Yanai:
AR DeepCalorieCam: An iOS App for Food Calorie Estimation with Augmented Reality. MMM (2) 2018: 352-356 - [c6]Ryosuke Tanno, Takumi Ege, Keiji Yanai:
AR DeepCalorieCam V2: food calorie estimation with CNN and AR-based actual size estimation. VRST 2018: 46:1-46:2 - 2017
- [c5]Keiji Yanai, Ryosuke Tanno:
Conditional Fast Style Transfer Network. ICMR 2017: 434-437 - [c4]Ryosuke Tanno, Shin Matsuo, Wataru Shimoda, Keiji Yanai:
DeepStyleCam: A Real-Time Style Transfer App on iOS. MMM (2) 2017: 446-449 - 2016
- [c3]Ryosuke Tanno, Koichi Okamoto, Keiji Yanai:
DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System. MADiMa @ ACM Multimedia 2016: 89 - [c2]Keiji Yanai, Ryosuke Tanno, Koichi Okamoto:
Efficient Mobile Implementation of A CNN-based Object Recognition System. ACM Multimedia 2016: 362-366 - [c1]Ryosuke Tanno, Keiji Yanai:
Caffe2C: A Framework for Easy Implementation of CNN-based Mobile Applications. MobiQuitous (Adjunct Proceedings) 2016: 159-164
Coauthor Index
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