1 citations · 2 across the 6 of their papers we have counts for
8 papers · 1 filter
Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning
Sora Iwamoto, Bisser Raytchev, Toru Tamaki +1
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 ar…
Rephrasing visual questions by specifying the entropy of the answer distribution
Kento Terao, Toru Tamaki, Bisser Raytchev +2
Visual question answering (VQA) is a task of answering a visual question that is a pair of question and image. Some visual questions are ambiguous and some are clear, and it may be…
On-line non-overlapping camera calibration net
Zhao Fangda, Toru Tamaki, Takio Kurita +2
We propose an easy-to-use non-overlapping camera calibration method. First, successive images are fed to a PoseNet-based network to obtain ego-motion of cameras between frames. Nex…
Improved Activity Forecasting for Generating Trajectories
Daisuke Ogawa, Toru Tamaki, Tsubasa Hirakawa +3
An efficient inverse reinforcement learning for generating trajectories is proposed based of 2D and 3D activity forecasting. We modify reward function with norm and propose c…
Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis
Toru Tamaki, Daisuke Ogawa, Bisser Raytchev +1
In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian…
Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images
Shohei Hayashi, Bisser Raytchev, Toru Tamaki +1
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 ac…