NaiLIA：基于密集意图描述和调色板查询的多模态美甲设计检索
NaiLIA: Multimodal Nail Design Retrieval Based on Dense Intent Descriptions and Palette Queries
AI总结：
NaiLIA通过密集意图描述和调色板查询实现美甲设计图像的多模态检索，提升检索精度。
摘要：
我们专注于基于密集意图描述的美甲设计图像检索任务，这些描述代表了多层的用户意图。这种任务具有挑战性，因为这些描述指定了无约束的涂色元素和预制造的装饰品，以及视觉特征、主题和整体印象。除了这些描述外，我们假设用户通过指定零个或多个颜色来提供调色板查询，从而表达微妙和连续的颜色细微差别。现有的视觉-语言基础模型往往难以整合此类描述和调色板。为此，我们提出了NaiLIA，一种用于美甲设计图像的多模态检索方法，该方法在检索过程中全面对齐密集意图描述和调色板查询。我们的方法引入了一种基于置信度分数的放松损失，用于未标记图像，这些图像可以与描述对齐。为了评估NaiLIA，我们构建了一个基准，包含从具有不同文化背景的人群中收集的10,625张图像。这些图像由超过200名标注者提供了长而密集的意图描述。实验结果表明，NaiLIA优于标准方法。
英文摘要：
We focus on the task of retrieving nail design images based on dense intent descriptions, which represent multi-layered user intent for nail designs. This is challenging because such descriptions specify unconstrained painted elements and pre-manufactured embellishments as well as visual characteristics, themes, and overall impressions. In addition to these descriptions, we assume that users provide palette queries by specifying zero or more colors via a color picker, enabling the expression of subtle and continuous color nuances. Existing vision-language foundation models often struggle to incorporate such descriptions and palettes. To address this, we propose NaiLIA, a multimodal retrieval method for nail design images, which comprehensively aligns with dense intent descriptions and palette queries during retrieval. Our approach introduces a relaxed loss based on confidence scores for unlabeled images that can align with the descriptions. To evaluate NaiLIA, we constructed a benchmark consisting of 10,625 images collected from people with diverse cultural backgrounds. The images were annotated with long and dense intent descriptions given by over 200 annotators. Experimental results demonstrate that NaiLIA outperforms standard methods.
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。
