Search Results for author: Toru Ogawa

Found 7 papers, 3 papers with code

Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications

3 code implementations9 May 2020 Kiyoharu Aizawa, Azuma Fujimoto, Atsushi Otsubo, Toru Ogawa, Yusuke Matsui, Koki Tsubota, Hikaru Ikuta

Manga, or comics, which are a type of multimodal artwork, have been left behind in the recent trend of deep learning applications because of the lack of a proper dataset.


Team PFDet's Methods for Open Images Challenge 2019

no code implementations25 Oct 2019 Yusuke Niitani, Toru Ogawa, Shuji Suzuki, Takuya Akiba, Tommi Kerola, Kohei Ozaki, Shotaro Sano

Using this method, the team PFDet achieved 3rd and 4th place in the instance segmentation and the object detection track, respectively.

Instance Segmentation Object +4

Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects

no code implementations CVPR 2019 Yusuke Niitani, Takuya Akiba, Tommi Kerola, Toru Ogawa, Shotaro Sano, Shuji Suzuki

However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector.

object-detection Object Detection

Object Detection for Comics using Manga109 Annotations

5 code implementations23 Mar 2018 Toru Ogawa, Atsushi Otsubo, Rei Narita, Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa

We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations.

Object object-detection +1

ChainerCV: a Library for Deep Learning in Computer Vision

2 code implementations28 Aug 2017 Yusuke Niitani, Toru Ogawa, Shunta Saito, Masaki Saito

Despite significant progress of deep learning in the field of computer vision, there has not been a software library that covers these methods in a unifying manner.

object-detection Object Detection +1

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