SE-MoLoRA：用于特定领域摄影评估的共享专家LoRA适配器

中文摘要 AI 辅助

视觉-语言模型能流畅描述图像，但因语义内容与审美判断相互纠缠，往往无法提供可行的摄影评论。本文提出SE-MoLoRA，一种用于特定领域摄影评估的模块化参数高效适配框架。该方法通过始终激活的共享LoRA专家，以及针对构图、光线和技术质量的路由适配器，将通用摄影知识与专业剩余判断分离开。轻量级查询路由器选择相关专家，无需训练单独的完整模型即可实现针对性评论。秩为64的共享适配器捕获广泛的摄影词汇，而秩为32的专家学习特定领域的剩余内容，并采用正交正则化惩罚以鼓励解耦表示。训练数据通过将Reddit摄影评论数据集蒸馏为带有领域标签的评论样本获得。在保留的评论生成任务上，SE-MoLoRA相较于整体LoRA将BERTScore-F1从0.2317提升至0.4215，在84.6%的成对比较中更受青睐，同时使用的激活参数少于单独的专家模型。基于SVD的消融研究表明，共享-专家分解和正交正则化减少了专家重叠。这些结果证明，模块化适配提升了多模态摄影评论的可控性和特异性。

英文摘要

Vision-language models can describe images fluently, but they often fail to provide actionable photographic critique because semantic content and aesthetic judgment remain entangled. We propose SE-MoLoRA, a modular parameter-efficient adaptation framework for domain-specific photographic assessment. The method separates general photographic knowledge from specialist residual judgments using an always-active shared LoRA expert and routed adapters for composition, lighting, and technical quality. A lightweight query router selects the relevant specialist, enabling targeted critique without training separate full models. A rank-64 shared adapter captures broad photographic vocabulary, while rank-32 specialists learn domain-specific residuals with an orthogonal regularization penalty that encourages disentangled representations. Training data is obtained by distilling the Reddit Photo Critique Dataset into domain-labeled critique samples. On held-out critique generation, SE-MoLoRA improves BERTScore-F1 from 0.2317 to 0.4215 over monolithic LoRA and is preferred in 84.6\% of pairwise comparisons, while using fewer active parameters than separate specialist models. SVD-based ablation study shows that shared-specialist decomposition and orthogonal regularization reduce expert overlap. These results demonstrate that modular adaptation improves controllability and specificity in multimodal photographic critique.

发表机构

• Faculty of Information Technology（信息技术学院）
• University of Jyväskylä（于韦斯屈莱大学）
• Adobe Research（奥多比研究院）
• University of Helsinki（赫尔辛基大学）

机构由 AI 辅助整理，请以论文原文为准。