3 citations · 7 across the 8 of their papers we have counts for
12 papers
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,…
ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video Recognition
Masahiro Mitsuhara, Tsubasa Hirakawa, Takayoshi Yamashita +1
It is difficult for people to interpret the decision-making in the inference process of deep neural networks. Visual explanation is one method for interpreting the decision-making…
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification
Naoki Okamoto, Soma Minami, Tsubasa Hirakawa +2
Mutual learning, in which multiple networks learn by sharing their knowledge, improves the performance of each network. However, the performance of ensembles of networks that have…
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning
Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita +2
Deep reinforcement learning (DRL) has great potential for acquiring the optimal action in complex environments such as games and robot control. However, it is difficult to analyze…
Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
Aly Magassouba, Komei Sugiura, Angelica Nakayama +4
Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. Th…
Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network
Tadashi Ogura, Aly Magassouba, Komei Sugiura +4
Domestic service robots (DSRs) are a promising solution to the shortage of home care workers. However, one of the main limitations of DSRs is their inability to interact naturally…