QPT V2：掩码图像建模在视觉评分中的进展
QPT V2: Masked Image Modeling Advances Visual Scoring
AI总结：
本文提出QPT V2框架，通过掩码图像建模提升视觉质量和审美评分能力，通过数据筛选、降质引入和模型结构调整，在11个下游基准测试中表现优异。
摘要：
质量评估和审美评估旨在评估视觉内容的感知质量和审美。当前基于学习的方法因标注数据稀缺而表现不佳，泛化能力差。尽管掩码图像建模（MIM）在多种高阶任务中取得了显著进展，但本文从新视角探讨其在质量和审美感知方面的潜力。为此，提出基于MIM的首个预训练框架QPT V2，提供统一的解决方案。通过筛选预训练数据、引入降质因素和修改模型结构，以捕捉多尺度质量和审美信息。在11个下游基准测试中，QPT V2在性能上优于当前最先进的方法和其他预训练范式。
英文摘要：
Quality assessment and aesthetics assessment aim to evaluate the perceived quality and aesthetics of visual content. Current learning-based methods suffer greatly from the scarcity of labeled data and usually perform sub-optimally in terms of generalization. Although masked image modeling (MIM) has achieved noteworthy advancements across various high-level tasks (e.g., classification, detection etc.). In this work, we take on a novel perspective to investigate its capabilities in terms of quality- and aesthetics-awareness. To this end, we propose Quality- and aesthetics-aware pretraining (QPT V2), the first pretraining framework based on MIM that offers a unified solution to quality and aesthetics assessment. To perceive the high-level semantics and fine-grained details, pretraining data is curated. To comprehensively encompass quality- and aesthetics-related factors, degradation is introduced. To capture multi-scale quality and aesthetic information, model structure is modified. Extensive experimental results on 11 downstream benchmarks clearly show the superior performance of QPT V2 in comparison with current state-of-the-art approaches and other pretraining paradigms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
