Search Results for author: Yoko SASAKI

Found 9 papers, 1 papers with code

Adaptive Future Frame Prediction with Ensemble Network

no code implementations13 Nov 2020 Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI

A common limitation of the existing learning-based approaches is a mismatch of training data and test data.

Deep Reactive Planning in Dynamic Environments

no code implementations31 Oct 2020 Kei Ota, Devesh K. Jha, Tadashi Onishi, Asako Kanezaki, Yusuke Yoshiyasu, Yoko SASAKI, Toshisada Mariyama, Daniel Nikovski

The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.

Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling

no code implementations28 Jul 2020 Yoshiki Masuyama, Yoshiaki Bando, Kohei Yatabe, Yoko Sasaki, Masaki Onishi, Yasuhiro Oikawa

By incorporating with the spatial information in multichannel audio signals, our method trains deep neural networks (DNNs) to distinguish multiple sound source objects.

Self-Supervised Learning

3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds

no code implementations21 Apr 2020 Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU

In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.

2D Object Detection 3D Object Detection +3

YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud

no code implementations21 Apr 2020 Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU

In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.

2D Object Detection 3D Object Detection +2

Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training

no code implementations11 Mar 2020 Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI

In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds.

Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path

no code implementations3 Mar 2020 Kei Ota, Yoko SASAKI, Devesh K. Jha, Yusuke Yoshiyasu, Asako Kanezaki

Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.

Efficient Exploration

Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model

no code implementations29 Aug 2019 Yoshiaki Bando, Yoko SASAKI, Kazuyoshi Yoshii

This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals.

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