41 citations · 44 across the 20 of their papers we have counts for
7 papers · 1 filter
Noisy Label Refinement with Semantically Reliable Synthetic Images
Yingxuan Li, Jiafeng Mao, Yusuke Matsui
Semantic noise in image classification datasets, where visually similar categories are frequently mislabeled, poses a significant challenge to conventional supervised learning appr…
Region-Wise Correspondence Prediction between Manga Line Art Images
Yingxuan Li, Jiafeng Mao, Qianru Qiu +1
Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks such as line art colorization an…
LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing
Liangyang Ouyang, Jiafeng Mao
Text-driven image editing enables users to flexibly modify visual content through natural language instructions, and is widely applied to tasks such as semantic object replacement,…
MangaDiT: Reference-Guided Line Art Colorization with Hierarchical Attention in Diffusion Transformers
Qianru Qiu, Jiafeng Mao, Kento Masui +1
Recent advances in diffusion models have significantly improved the performance of reference-guided line art colorization. However, existing methods still struggle with region-leve…
Exploring Palette based Color Guidance in Diffusion Models
Qianru Qiu, Jiafeng Mao, Xueting Wang
With the advent of diffusion models, Text-to-Image (T2I) generation has seen substantial advancements. Current T2I models allow users to specify object colors using linguistic colo…
Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling
Mingzhuo Li, Guang Li, Jiafeng Mao +3
To alleviate the reliance of deep neural networks on large-scale datasets, dataset distillation aims to generate compact, high-quality synthetic datasets that can achieve comparabl…
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory
Mingzhuo Li, Guang Li, Jiafeng Mao +2
Dataset distillation enables the training of deep neural networks with comparable performance in significantly reduced time by compressing large datasets into small and representat…