34 citations · 102 across the 29 of their papers we have counts for
5 papers · 1 filter
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes
Shungo Fujii, Yasunori Ishii, Kazuki Kozuka +3
Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning. Methods that generate mixed data from multiple data sets,…
Few-shot Adaptive Object Detection with Cross-Domain CutMix
Yuzuru Nakamura, Yasunori Ishii, Yuki Maruyama +1
In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive. Therefore, existing large-scale datasets a…
Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch
Yuya Maruyama, Hiroshi Fukui, Tsubasa Hirakawa +3
Robot navigation with deep reinforcement learning (RL) achieves higher performance and performs well under complex environment. Meanwhile, the interpretation of the decision-making…
Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net
Risako Tanigawa, Yasunori Ishii, Kazuki Kozuka +1
Color images are easy to understand visually and can acquire a great deal of information, such as color and texture. They are highly and widely used in tasks such as segmentation.…
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
Risako Tanigawa, Yasunori Ishii, Kazuki Kozuka +1
In action understanding in indoor, we have to recognize human pose and action considering privacy. Although camera images can be used for highly accurate human action recognition,…