5 citations · 5 across the 5 of their papers we have counts for
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GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
Qiaosi Yi, Shuai Li, Rongyuan Wu +3
Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step ge…
Restoration Adaptation for Semantic Segmentation on Low Quality Images
Kai Guan, Rongyuan Wu, Shuai Li +3
In real-world scenarios, the performance of semantic segmentation often deteriorates when processing low-quality (LQ) images, which may lack clear semantic structures and high-freq…
DPO-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang +5
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 th…
Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training
Qiaosi Yi, Shuai Li, Rongyuan Wu +3
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 met…
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation
Pengfei Wang, Yuxi Wang, Shuai Li +3
The scarcity of large-scale 3D-text paired data poses a great challenge on open vocabulary 3D scene understanding, and hence it is popular to leverage internet-scale 2D data and tr…
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Rongyuan Wu, Tao Yang, Lingchen Sun +3
Owe to the powerful generative priors, the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution proble…