no code implementations • 4 Mar 2024 • Tong Zheng, Shusaku Sone, Yoshitaka Ushiku, Yuki Oba, Jiaxin Ma
This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data.
1 code implementation • 26 Oct 2023 • Yuxin Zuo, Bei Li, Chuanhao Lv, Tong Zheng, Tong Xiao, Jingbo Zhu
This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete.
1 code implementation • 23 Oct 2023 • Tong Zheng, Bei Li, Huiwen Bao, Weiqiao Shan, Tong Xiao, Jingbo Zhu
The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead.
Ranked #23 on Machine Translation on WMT2014 English-German
1 code implementation • 20 Dec 2022 • Tong Zheng, Bei Li, Huiwen Bao, Tong Xiao, Jingbo Zhu
In this paper, we propose a novel architecture, the Enhanced Interactive Transformer (EIT), to address the issue of head degradation in self-attention mechanisms.
1 code implementation • 19 Jun 2022 • Bei Li, Tong Zheng, Yi Jing, Chengbo Jiao, Tong Xiao, Jingbo Zhu
In this work, we define those scales in different linguistic units, including sub-words, words and phrases.
no code implementations • 9 Jan 2022 • Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku MORI
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
no code implementations • 20 Oct 2020 • Tong Zheng, Hirohisa ODA, Masahiro Oda, Shota NAKAMURA, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI
Unsupervised SR methods are required that do not need paired LR and HR images.
no code implementations • 7 Apr 2020 • Tong ZHENG, Hirohisa ODA, Takayasu MORIYA, Takaaki SUGINO, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI
This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes.
no code implementations • 30 Dec 2019 • Tong Zheng, Hirohisa ODA, Takayasu MORIYA, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Horitsugu Takabatake, Hiroshi NATORI, Kensaku MORI
This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes.
1 code implementation • 12 May 2019 • Jun Zhang, Tong Zheng, Shengping Zhang, Meng Wang
First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches.