34 citations · 77 across the 22 of their papers we have counts for
3 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,…
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…
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition
Tomoya Nitta, Tsubasa Hirakawa, Hironobu Fujiyoshi +1
In this paper we propose an extension of the Attention Branch Network (ABN) by using instance segmentation for generating sharper attention maps for action recognition. Methods for…