11 papers
Cell Instance Segmentation via Multi-Task Image-to-Image Schrödinger Bridge
Hayato Inoue, Shota Harada, Shumpei Takezaki +1
Existing cell instance segmentation pipelines typically combine deterministic predictions with post-processing, which imposes limited explicit constraints on the global structure o…
SCoRe: Clean Image Generation from Diffusion Models Trained on Noisy Images
Yuta Matsuzaki, Seiichi Uchida, Shumpei Takezaki
Diffusion models trained on noisy datasets often reproduce high-frequency training artifacts, significantly degrading generation quality. To address this, we propose SCoRe (Spectra…
VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images
Soichi Mita, Shumpei Takezaki, Ryoma Bise
Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to es…
Few-Part-Shot Font Generation
Masaki Akiba, Shumpei Takezaki, Daichi Haraguchi +1
This paper proposes a novel model of few-part-shot font generation, which designs an entire font based on a set of partial design elements, i.e., partial shapes. Unlike conventiona…
NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models
Shumpei Takezaki, Ryoma Bise, Shinnosuke Matsuo
In this study, we propose a novel data augmentation method that introduces the concept of CutMix into the generation process of diffusion models, thereby exploiting both the abilit…
Inverse Scene Text Removal
Takumi Yoshimatsu, Shumpei Takezaki, Seiichi Uchida
Scene text removal (STR) aims to erase textual elements from images. It was originally intended for removing privacy-sensitiveor undesired texts from natural scene images, but is n…