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Self-Relaxed Joint Training: Sample Selection for Severity Estimation with Ordinal Noisy Labels
Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida
Severity level estimation is a crucial task in medical image diagnosis. However, accurately assigning severity class labels to individual images is very costly and challenging. Con…
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model
Masahito Toba, Seiichi Uchida, Hideaki Hayashi
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate conf…
NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging
Takahiro Shirakawa, Seiichi Uchida
Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-imag…
Cross-Domain Image Conversion by CycleDM
Sho Shimotsumagari, Shumpei Takezaki, Daichi Haraguchi +1
The purpose of this paper is to enable the conversion between machine-printed character images (i.e., font images) and handwritten character images through machine learning. For th…
What Text Design Characterizes Book Genres?
Daichi Haraguchi, Brian Kenji Iwana, Seiichi Uchida
This study analyzes the relationship between non-verbal information (e.g., genres) and text design (e.g., font style, character color, etc.) through the classification of book genr…
Impression-CLIP: Contrastive Shape-Impression Embedding for Fonts
Yugo Kubota, Daichi Haraguchi, Seiichi Uchida
Fonts convey different impressions to readers. These impressions often come from the font shapes. However, the correlation between fonts and their impression is weak and unstable b…