41 citations · 44 across the 20 of their papers we have counts for
20 papers
Self-Supervised Representation-Guided Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility. Most existing methods target randomly initialized networks…
SAS: Semantic-aware Sampling for Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational and storage costs due to large-s…
Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks
Mingzhuo Li, Guang Li, Linfeng Ye +4
In this paper, we propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task, therefore improving the performance…
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…
Reward Incremental Learning in Text-to-Image Generation
Maorong Wang, Jiafeng Mao, Xueting Wang +1
The recent success of denoising diffusion models has significantly advanced text-to-image generation. While these large-scale pretrained models show excellent performance in genera…
Dealing with Synthetic Data Contamination in Online Continual Learning
Maorong Wang, Nicolas Michel, Jiafeng Mao +1
Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of…
Difficulty Controlled Diffusion Model for Synthesizing Effective Training Data
Zerun Wang, Jiafeng Mao, Xueting Wang +1
Generative models have become a powerful tool for synthesizing training data in computer vision tasks. Current approaches solely focus on aligning generated images with the target…
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
Zerun Wang, Liuyu Xiang, Lang Huang +3
Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) s…
Training-Free Sketch-Guided Diffusion with Latent Optimization
Sandra Zhang Ding, Jiafeng Mao, Kiyoharu Aizawa
Based on recent advanced diffusion models, Text-to-image (T2I) generation models have demonstrated their capabilities to generate diverse and high-quality images. However, leveragi…
From Obstacles to Resources: Semi-supervised Learning Faces Synthetic Data Contamination
Zerun Wang, Jiafeng Mao, Liuyu Xiang +1
Semi-supervised learning (SSL) can improve model performance by leveraging unlabeled images, which can be collected from public image sources with low costs. In recent years, synth…
The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization
Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa
Text-to-image diffusion models allow users control over the content of generated images. Still, text-to-image generation occasionally leads to generation failure requiring users to…
Guided Image Synthesis via Initial Image Editing in Diffusion Model
Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa
Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images. While previous research has primarily focused on improving the control of…
Training-Free Location-Aware Text-to-Image Synthesis
Jiafeng Mao, Xueting Wang
Current large-scale generative models have impressive efficiency in generating high-quality images based on text prompts. However, they lack the ability to precisely control the si…
Noisy Annotation Refinement for Object Detection
Jiafeng Mao, Qing Yu, Yoko Yamakata +1
Supervised training of object detectors requires well-annotated large-scale datasets, whose production is costly. Therefore, some efforts have been made to obtain annotations in ec…