29 citations · 58 across the 8 of their papers we have counts for
8 papers
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
Hiromichi Kamata, Yuiko Sakuma, Akio Hayakawa +2
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D. Our method is designed for a novel task, which is to convert a given 3D scene to another scene according to…
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter
Yuiko Sakuma, Masato Ishii, Takuya Narihira
We address the challenge of training a large supernet for the object detection task, using a relatively small amount of training data. Specifically, we propose an efficient superne…
Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
Naoki Matsunaga, Masato Ishii, Akio Hayakawa +2
Our goal is to develop fine-grained real-image editing methods suitable for real-world applications. In this paper, we first summarize four requirements for these methods and propo…
Semi-supervised learning by selective training with pseudo labels via confidence estimation
Masato Ishii
We propose a novel semi-supervised learning (SSL) method that adopts selective training with pseudo labels. In our method, we generate hard pseudo-labels and also estimate their co…
Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision
Andrew Shin, Masato Ishii, Takuya Narihira
Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years. Its succes…
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux +14
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design…
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics
Masato Ishii, Masashi Sugiyama
In this paper, we propose a novel domain adaptation method for the source-free setting. In this setting, we cannot access source data during adaptation, while unlabeled target data…
Zero-shot Domain Adaptation Based on Attribute Information
Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a sp…