11 papers
GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
Takuro Kawada, Shunsuke Kitada, Hitoshi Iyatomi
Graphical Abstracts (GAs) visually summarize the key findings of academic papers, playing a crucial role in facilitating the understanding of research content. Recently, advancemen…
SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Papers
Takuro Kawada, Shunsuke Kitada, Sota Nemoto +1
Graphical Abstracts (GAs) play a crucial role in visually conveying the key findings of scientific papers. Although recent research increasingly incorporates visual materials such…
TAUE: Training-free Noise Transplant and Cultivation Diffusion Model
Daichi Nagai, Ryugo Morita, Shunsuke Kitada +1
Despite the remarkable success of text-to-image diffusion models, their output of a single, flattened image remains a critical bottleneck for professional applications requiring la…
Robust Plant Disease Diagnosis with Few Target-Domain Samples
Takafumi Nogami, Satoshi Kagiwada, Hitoshi Iyatomi
Various deep learning-based systems have been proposed for accurate and convenient plant disease diagnosis, achieving impressive performance. However, recent studies show that thes…
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box Training
Hideaki Okamoto, Quan Huu Cap, Takakiyo Nomura +3
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be dia…
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
Sota Nemoto, Shunsuke Kitada, Hitoshi Iyatomi
Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER ex…