Search Results for author: Tetsuya Takiguchi

Found 11 papers, 2 papers with code

Building a Knowledge-Based Dialogue System with Text Infilling

no code implementations SIGDIAL (ACL) 2022 Qiang Xue, Tetsuya Takiguchi, Yasuo Ariki

However, knowledge-based dialog systems sometimes generate responses without using the retrieved knowledge. In this work, we propose a method in which the knowledge-based dialogue system can constantly utilize the retrieved knowledge using text infilling .

Sentence Text Infilling

Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation

no code implementations22 Jun 2022 Weihao Zhuang, Tristan Hascoet, Ryoichi Takashima, Tetsuya Takiguchi

Recent works have shown the ability of Implicit Neural Representations (INR) to carry meaningful representations of signal derivatives.

Optical Flow Estimation Video Compression +1

Current Source Localization Using Deep Prior with Depth Weighting

no code implementations26 Mar 2022 Rio Yamana, Hajime Yano, Ryoichi Takashima, Tetsuya Takiguchi, Seiji Nakagawa

This paper proposes a novel neuronal current source localization method based on Deep Prior that represents a more complicated prior distribution of current source using convolutional networks.

FasterRCNN Monitoring of Road Damages: Competition and Deployment

2 code implementations22 Oct 2020 Hascoet Tristan, Yihao Zhang, Persch Andreas, Ryoichi Takashima, Tetsuya Takiguchi, Yasuo Ariki

Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world.

Road Damage Detection

Reversible designs for extreme memory cost reduction of CNN training

no code implementations24 Oct 2019 Tristan Hascoet, Quentin Febvre, Yasuo Ariki, Tetsuya Takiguchi

This new kind of architecture enables training large neural networks on very limited memory, opening the door for neural network training on embedded devices or non-specialized hardware.

On zero-shot recognition of generic objects

1 code implementation CVPR 2019 Tristan Hascoet, Yasuo Ariki, Tetsuya Takiguchi

We discuss how the presence of this new form of bias allows for a trivial solution to the standard benchmark and conclude on the need for a new benchmark.

Object Recognition Zero-Shot Learning

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