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Qiaosi Yi

7 accepted papers

2026

CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning

ICML 2026poster

Image editing has achieved impressive results with the development of large-scale generative models. However, existing models mainly focus on the editing effects of intended objects and regions, often leading to unwanted changes in unintended regions. We present a post-training framework for \textbf…

Cited by 0SourceScholar
2026

GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution

CVPR 2026

Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step generative ISR, while one-step generative ISR remains underexplored due to its limited stochasticity. In addition, RL meth

Cited by 0SourcecodeScholar
2025

DNAEdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing

NeurIPS 2025spotlight

Leveraging the powerful generation capability of large-scale pretrained text-to-image models, training-free methods have demonstrated impressive image editing results. Conventional diffusion-based methods, as well as recent rectified flow (RF)-based methods, typically reverse synthesis trajectories…

Cited by 0SourceScholar
2025

DP²O-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution

NeurIPS 2025poster

Benefiting from pre-trained text-to-image (T2I) diffusion models, real-world image super-resolution (Real-ISR) methods can synthesize rich and realistic details. However, due to the inherent stochasticity of T2I models, different noise inputs often lead to outputs with varying perceptual quality. Al…

Cited by 0SourceScholar
2025

Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training

ICCV 2025poster

Impressive results on real-world image super-resolution (Real-ISR) have been achieved by employing pre-trained stable diffusion (SD) models. However, one critical issue of such methods lies in their poor reconstruction of image fine structures, such as small characters and textures, due to the aggre…

2025

Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach

CVPR 2025poster

Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR objectives in the training process, struggling to balance pixel-wise fidelity and perceptual quality. Meanwhile, users have…

2021

Structure-Preserving Deraining With Residue Channel Prior Guidance

ICCV 2021poster

Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these method…

Cited by 144PDFcodeScholar