no code implementations • ICCV 2015 • Yoshitaka Ushiku, Masataka Yamaguchi, Yusuke Mukuta, Tatsuya Harada
In order to overcome the shortage of training samples, CoSMoS obtains a subspace in which (a) all feature vectors associated with the same phrase are mapped as mutually close, (b) classifiers for each phrase are learned, and (c) training samples are shared among co-occurring phrases.
no code implementations • 30 Mar 2016 • Andrew Shin, Masataka Yamaguchi, Katsunori Ohnishi, Tatsuya Harada
The workflow of extracting features from images using convolutional neural networks (CNN) and generating captions with recurrent neural networks (RNN) has become a de-facto standard for image captioning task.
no code implementations • ICCV 2017 • Masataka Yamaguchi, Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
In this paper, we address the problem of spatio-temporal person retrieval from multiple videos using a natural language query, in which we output a tube (i. e., a sequence of bounding boxes) which encloses the person described by the query.
1 code implementation • 31 Oct 2017 • Andrew Shin, Leopold Crestel, Hiroharu Kato, Kuniaki Saito, Katsunori Ohnishi, Masataka Yamaguchi, Masahiro Nakawaki, Yoshitaka Ushiku, Tatsuya Harada
Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians.
Sound Multimedia Audio and Speech Processing
no code implementations • 14 Dec 2018 • Masataka Yamaguchi, Yuma Koizumi, Noboru Harada
To address this difficulty, we propose AdaFlow, a new DNN-based density estimator that can be easily adapted to the change of the distribution.
no code implementations • 19 Jul 2019 • Yuma Koizumi, Shoichiro Saito, Masataka Yamaguchi, Shin Murata, Noboru Harada
The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch.
no code implementations • ICCV 2019 • Masataka Yamaguchi, Go Irie, Takahito Kawanishi, Kunio Kashino
The most popular subspace clustering framework in recent years is the graph clustering-based approach, which performs subspace clustering in two steps: graph construction and graph clustering.