6 papers
GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
Yongsong Huang, Tzu-Hsuan Peng, Tomo Miyazaki +4
Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture…
U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonization
Weiwei Ma, Xiaobing Yu, Peijie Qiu +7
In clinical practice, medical segmentation datasets are often limited and heterogeneous, with variations in modalities, protocols, and anatomical targets across institutions. Exist…
Class-agnostic 3D Segmentation by Granularity-Consistent Automatic 2D Mask Tracking
Juan Wang, Yasutomo Kawanishi, Tomo Miyazaki +2
3D instance segmentation is an important task for real-world applications. To avoid costly manual annotations, existing methods have explored generating pseudo labels by transferri…
GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution
Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu +1
Infrared Image Super-Resolution (IRSR) is challenged by the low contrast and sparse textures of infrared data, requiring robust long-range modeling to maintain global coherence. Wh…
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies
Zhengmi Tang, Yuto Mitsui, Tomo Miyazaki +1
Most existing text recognition methods are trained on large-scale synthetic datasets due to the scarcity of labeled real-world datasets. Synthetic images, however, cannot faithfull…
Towards Cross-Domain Multi-Targeted Adversarial Attacks
Taïga Gonçalves, Tomo Miyazaki, Shinichiro Omachi
Multi-targeted adversarial attacks aim to mislead classifiers toward specific target classes using a single perturbation generator with a conditional input specifying the desired t…