1 code implementation • 26 Aug 2021 • Sora Iwamoto, Bisser Raytchev, Toru Tamaki, Kazufumi Kaneda
In this paper we propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning so that the uncertainty estimates are fed back to the system to resample the training data more densely in areas where uncertainty is high.
1 code implementation • 12 Apr 2020 • Kento Terao, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shun'ichi Satoh
Then we use state-of-the-art methods to determine the accuracy and the entropy of the answer distributions for each cluster.
no code implementations • 10 Apr 2020 • Kento Terao, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shun'ichi Satoh
The proposed model rephrases a source question given with an image so that the rephrased question has the ambiguity (or entropy) specified by users.
no code implementations • 19 Feb 2020 • Zhao Fangda, Toru Tamaki, Takio Kurita, Bisser Raytchev, Kazufumi Kaneda
First, successive images are fed to a PoseNet-based network to obtain ego-motion of cameras between frames.
no code implementations • 12 Dec 2019 • Toru Tamaki, Daisuke Ogawa, Bisser Raytchev, Kazufumi Kaneda
In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents.
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.
no code implementations • 27 Sep 2019 • Shohei Hayashi, Bisser Raytchev, Toru Tamaki, Kazufumi Kaneda
In this paper we propose a novel deep learning-based algorithm for biomedical image segmentation which uses a sequential attention mechanism able to shift the focus of attention across the image in a selective way, allowing subareas which are more difficult to classify to be processed at increased resolution.
no code implementations • 27 Feb 2018 • Daisuke Ogawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda
Next, using learned behavior models and a hidden Markov model, we segment a trajectory into semantic segments.
1 code implementation • 28 Nov 2017 • Kenji Matsui, Toru Tamaki, Gwladys Auffret, Bisser Raytchev, Kazufumi Kaneda
We propose a feature for action recognition called Trajectory-Set (TS), on top of the improved Dense Trajectory (iDT).
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.