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20222026
most citedQMamba: On First Exploration of Vision Mamba for Image Quality Assessment

2 citations · 8 across the 29 of their papers we have counts for

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Showing 2024 · cs.CVShow all

6 papers · 2 filters

cs.CV2024

LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents

Bingchen Li, Xin Li, Yiting Lu +1

We present the first loss agent, dubbed LossAgent, for low-level image processing tasks, e.g., image super-resolution and restoration, intending to achieve any customized optimizat…

cs.CV2024

Priorformer: A UGC-VQA Method with content and distortion priors

Yajing Pei, Shiyu Huang, Yiting Lu +2

User Generated Content (UGC) videos are susceptible to complicated and variant degradations and contents, which prevents the existing blind video quality assessment (BVQA) models f…

cs.CV2024★ 2 cited

QMamba: On First Exploration of Vision Mamba for Image Quality Assessment

Fengbin Guan, Xin Li, Zihao Yu +2

In this work, we take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assessment (IQA), aiming at observing and exca…

cs.CV2024

PromptCIR: Blind Compressed Image Restoration with Prompt Learning

Bingchen Li, Xin Li, Yiting Lu +5

Blind Compressed Image Restoration (CIR) has garnered significant attention due to its practical applications. It aims to mitigate compression artifacts caused by unknown quality f…

cs.CV2024★ 2 cited

VisualCritic: Making LMMs Perceive Visual Quality Like Humans

Zhipeng Huang, Zhizheng Zhang, Yiting Lu +3

At present, large multimodal models (LMMs) have exhibited impressive generalization capabilities in understanding and generating visual signals. However, they currently still lack…

cs.CV2024

Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy

Zihao Yu, Fengbin Guan, Yiting Lu +2

The objective of non-reference video quality assessment is to evaluate the quality of distorted video without access to reference high-definition references. In this study, we intr…