99 citations · 119 across the 3 of their papers we have counts for
7 papers
Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications
Kiyoharu Aizawa, Azuma Fujimoto, Atsushi Otsubo +4
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. Hence, we…
Team PFDet's Methods for Open Images Challenge 2019
Yusuke Niitani, Toru Ogawa, Shuji Suzuki +4
We present the instance segmentation and the object detection method used by team PFDet for Open Images Challenge 2019. We tackle a massive dataset size, huge class imbalance and f…
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Seiya Tokui, Ryosuke Okuta, Takuya Akiba +7
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intend…
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
Yusuke Niitani, Takuya Akiba, Tommi Kerola +3
Efficient and reliable methods for training of object detectors are in higher demand than ever, and more and more data relevant to the field is becoming available. However, large d…
PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track
Takuya Akiba, Tommi Kerola, Yusuke Niitani +3
We present a large-scale object detection system by team PFDet. Our system enables training with huge datasets using 512 GPUs, handles sparsely verified classes, and massive class…
Object Detection for Comics using Manga109 Annotations
Toru Ogawa, Atsushi Otsubo, Rei Narita +3
With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image unders…