16 citations · 20 across the 7 of their papers we have counts for
9 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…
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…
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…
DDD: Discriminative Difficulty Distance for plant disease diagnosis
Yuji Arima, Satoshi Kagiwada, Hitoshi Iyatomi
Recent studies on plant disease diagnosis using machine learning (ML) have highlighted concerns about the overestimated diagnostic performance due to inappropriate data partitionin…
Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis
Shoma Kudo, Satoshi Kagiwada, Hitoshi Iyatomi
Numerous studies have explored image-based automated systems for plant disease diagnosis, demonstrating impressive diagnostic capabilities. However, recent large-scale analyses hav…