29 citations · 58 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…