组合缓存：用于免训练开放词汇属性检测
Compositional Caching for Training-free Open-vocabulary Attribute Detection
AI总结：
提出组合缓存（ComCa），一种免训练的开放词汇属性检测方法，利用网络数据库和大型语言模型构建辅助缓存并聚合软属性标签，显著优于零样本和缓存基线，可与训练方法竞争。
摘要：
属性检测对于许多计算机视觉任务至关重要，因为它使系统能够描述颜色、纹理和材料等属性。当前方法通常依赖劳动密集型的标注过程，这些过程固有地受限：对象可以在任意细节级别上描述（例如，颜色与色调），当标注者未受到仔细指导时会导致歧义。此外，它们在一组预定义的属性内运行，降低了对不可预见下游应用的可扩展性和适应性。我们提出了组合缓存（ComCa），一种用于开放词汇属性检测的免训练方法，克服了这些限制。ComCa仅需目标属性和对象列表作为输入，利用它们通过利用网络规模数据库和大型语言模型来确定属性-对象兼容性，从而填充辅助图像缓存。为了考虑属性的组合性质，缓存图像接收软属性标签。这些标签在推理时基于输入图像与缓存图像之间的相似性进行聚合，细化底层视觉-语言模型（VLM）的预测。重要的是，我们的方法是模型无关的，兼容各种VLM。在公共数据集上的实验表明，ComCa显著优于零样本和基于缓存的基线，与近期基于训练的方法竞争，证明了一种精心设计的免训练方法可以成功解决开放词汇属性检测问题。
英文摘要：
Attribute detection is crucial for many computer vision tasks, as it enables systems to describe properties such as color, texture, and material. Current approaches often rely on labor-intensive annotation processes which are inherently limited: objects can be described at an arbitrary level of detail (e.g., color vs. color shades), leading to ambiguities when the annotators are not instructed carefully. Furthermore, they operate within a predefined set of attributes, reducing scalability and adaptability to unforeseen downstream applications. We present Compositional Caching (ComCa), a training-free method for open-vocabulary attribute detection that overcomes these constraints. ComCa requires only the list of target attributes and objects as input, using them to populate an auxiliary cache of images by leveraging web-scale databases and Large Language Models to determine attribute-object compatibility. To account for the compositional nature of attributes, cache images receive soft attribute labels. Those are aggregated at inference time based on the similarity between the input and cache images, refining the predictions of underlying Vision-Language Models (VLMs). Importantly, our approach is model-agnostic, compatible with various VLMs. Experiments on public datasets demonstrate that ComCa significantly outperforms zero-shot and cache-based baselines, competing with recent training-based methods, proving that a carefully designed training-free approach can successfully address open-vocabulary attribute detection.
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。
