CLAY：视觉语言嵌入空间中的条件视觉相似度调制
CLAY: Conditional Visual Similarity Modulation in Vision-Language Embedding Space
把预训练 VLM 的嵌入空间当作文本条件的相似度空间，无需训练即可用固定视觉嵌入做多条件组合检索；对做图文检索、个性化搜索或需要按用户意图动态调整相似度度量的视觉研究者，这是一个可直接复用的免训练条件化思路。
原文依据 reframes the embedding space of pretrained Vision-Language Models (VLMs) as a text-conditional similarity space without additional training
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论人类对视觉相似性的感知本质上是自适应且主观的，取决于用户的兴趣与关注点。然而，大多数图像检索系统无法反映这种灵活性，它们依赖固定的、单一的度量，无法同时纳入多种条件。为解决这一问题，我们提出 CLAY，一种自适应相似度计算方法，它在无需额外训练的情况下，将预训练视觉语言模型（VLM）的嵌入空间重新构建为文本条件的相似度空间。该设计将文本条件化过程与视觉特征提取分离，使得在视觉嵌入固定的情况下能够进行高效且多条件的检索。我们还构建了一个合成评测数据集 CLAY-EVAL，用于在多样化的条件检索设定下进行全面评估。在标准数据集和我们提出的数据集上的实验表明，与先前工作相比，CLAY 取得了最先进的检索准确率，并具有显著的计算效率。
英文原文摘要 1005 字符
Human perception of visual similarity is inherently adaptive and subjective, depending on the users' interests and focus. However, most image retrieval systems fail to reflect this flexibility, relying on a fixed, monolithic metric that cannot incorporate multiple conditions simultaneously. To address this, we propose CLAY, an adaptive similarity computation method that reframes the embedding space of pretrained Vision-Language Models (VLMs) as a text-conditional similarity space without additional training. This design separates the textual conditioning process and visual feature extraction, allowing highly efficient and multi-conditioned retrieval with fixed visual embeddings. We also construct a synthetic evaluation dataset CLAY-EVAL, for comprehensive assessment under diverse conditioned retrieval settings. Experiments on standard datasets and our proposed dataset show that CLAY achieves state-of-the art retrieval accuracy and notable computational efficiency compared to previous works.