no code implementations • 24 Jun 2023 • Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Komei Sugiura
The decoder in AQT utilizes action queries, which represent the information of each action, as queries.
no code implementations • 4 Jun 2023 • Kohei Hattori, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
In this study, based on a deep-learning model that incorporates the knowledge of experts, a method by which a learner "learns from AI" the grounds for its decisions is proposed.
no code implementations • 16 Feb 2023 • Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Yasunori Ishii, Kazuki Kozuka
Adversarial training is a popular and straightforward technique to defend against the threat of adversarial examples.
no code implementations • 12 Sep 2022 • Shungo Fujii, Yasunori Ishii, Kazuki Kozuka, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning.
no code implementations • 27 Jul 2022 • Tomoya Nitta, Tsubasa Hirakawa, Hironobu Fujiyoshi, Toru Tamaki
Experimental results with UCF101 and SSv2 shows that the generated maps by the proposed method are much clearer qualitatively and quantitatively than those of the original ABN.
1 code implementation • 29 Oct 2021 • Masahiro Mitsuhara, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
It is difficult for people to interpret the decision-making in the inference process of deep neural networks.
no code implementations • 27 Mar 2021 • Naoki Okamoto, Soma Minami, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
In this study, we propose an ensemble method using knowledge transfer to improve the accuracy of ensembles by introducing a loss design that promotes diversity among networks in mutual learning.
no code implementations • 6 Mar 2021 • Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Komei Sugiura
A3C consists of a feature extractor that extracts features from an image, a policy branch that outputs the policy, and a value branch that outputs the state value.
no code implementations • 12 Feb 2021 • Aly Magassouba, Komei Sugiura, Angelica Nakayama, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Hisashi Kawai
Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task.
no code implementations • 9 Jul 2020 • Tadashi Ogura, Aly Magassouba, Komei Sugiura, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Hisashi Kawai
Domestic service robots (DSRs) are a promising solution to the shortage of home care workers.
no code implementations • 12 Dec 2019 • Daisuke Ogawa, Toru Tamaki, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Ken Yoda
An efficient inverse reinforcement learning for generating trajectories is proposed based of 2D and 3D activity forecasting.
1 code implementation • 10 Sep 2019 • Soma Minami, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
To achieve the knowledge transfer, we propose a novel graph representation called knowledge transfer graph that provides a unified view of the knowledge transfer and has the potential to represent diverse knowledge transfer patterns.
no code implementations • 9 May 2019 • Masahiro Mitsuhara, Hiroshi Fukui, Yusuke Sakashita, Takanori Ogata, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
As a result, the fine-tuned network can output an attention map that takes into account human knowledge.
3 code implementations • CVPR 2019 • Hiroshi Fukui, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
ABN can be applicable to several image recognition tasks by introducing a branch for attention mechanism and is trainable for the visual explanation and image recognition in end-to-end manner.
no code implementations • 1 Nov 2018 • Tsubasa Hirakawa, Takayoshi Yamashita, Toru Tamaki, Hironobu Fujiyoshi
Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths.
no code implementations • 15 Dec 2016 • Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
A computer-aided diagnosis (CAD) system that provides an objective measure to endoscopists during colorectal endoscopic examinations would be of great value.
no code implementations • 8 Nov 2016 • Toru Tamaki, Shoji Sonoyama, Takio Kurita, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka, Kazuaki Chayama
This paper proposes a method for domain adaptation that extends the maximum margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2 distance constraints between samples of different domains; thus, our method is denoted as MMDTL2.
no code implementations • 24 Aug 2016 • Toru Tamaki, Shoji Sonoyama, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
In this paper we report results for recognizing colorectal NBI endoscopic images by using features extracted from convolutional neural network (CNN).
no code implementations • 24 Aug 2016 • Shoji Sonoyama, Toru Tamaki, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
In this paper we propose a method for transfer learning of endoscopic images.