activity
20222026
most citedMANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SE2026

SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving

Chaofan Tao, Jierun Chen, Yuxin Jiang +11

We present SWE-Lego, a supervised fine-tuning (SFT) recipe designed to achieve state-ofthe-art performance in software engineering (SWE) issue resolving. In contrast to prevalent m…

cs.CL2025

From Denoising to Refining: A Corrective Framework for Vision-Language Diffusion Model

Yatai Ji, Teng Wang, Yuying Ge +4

Discrete diffusion models have emerged as a promising direction for vision-language tasks, offering bidirectional context modeling and theoretical parallelization. However, their p…

cs.CV2025

DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables

Sidi Yang, Binxiao Huang, Yulun Zhang +3

While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requir…

cs.CV20221 cited

Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

Shanshan Lao, Yuan Gong, Shuwei Shi +5

Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. Unfortunately, there is a performance drop when assessing the distortion images gen…

cs.CV20223 cited

MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

Sidi Yang, Tianhe Wu, Shuwei Shi +5

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA method…