层次概念嵌入与追踪用于可解释图像分类
Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification
HCEP 利用视觉语言模型的潜空间，通过层次化稀疏编码恢复图像中的概念，为设计可解释分类模型提供了新思路；其几何构造和恢复条件可推广到其他需要结构化概念表示的任务。
原文依据 we propose Hierarchical Concept Embedding & Pursuit (HCEP), a framework that induces a hierarchy of concept embeddings in the latent space and performs hierarchical sparse coding
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论设计即解释的模型在计算机视觉中日益受到关注，因为它们能为预测提供忠实的解释。在图像分类中，这类模型通常从图像中恢复人类可理解的概念，并利用这些概念进行分类。稀疏概念恢复方法利用视觉语言模型的潜空间，将图像嵌入表示为概念嵌入的稀疏组合。然而，这些方法忽略了语义概念的层次结构，可能产生与层次结构不一致的解释，即使预测正确。在本工作中，我们提出了层次概念嵌入与追踪（HCEP）框架，该框架在潜空间中诱导概念嵌入的层次结构，并执行层次稀疏编码以恢复图像中存在的概念。给定语义概念的层次结构，我们引入了相应嵌入层次结构的几何构造。在真实概念形成层次结构中一条根路径的假设下，我们推导了在嵌入空间中恢复它们的充分条件。我们进一步表明，层次稀疏编码能可靠地恢复层次概念嵌入，而标准稀疏编码则失败。在真实数据集上的实验表明，与现有方法相比，HCEP 提高了概念精确率和召回率，同时保持了有竞争力的分类准确率。此外，当可用于概念估计和分类器训练的样本数量有限时，HCEP 实现了更优的分类准确率和概念恢复。我们的结果表明，将层次结构纳入稀疏概念恢复可带来更忠实和可解释的图像分类模型。
英文原文摘要 1772 字符
Interpretable-by-design models are gaining traction in computer vision because they provide faithful explanations for their predictions. In image classification, these models typically recover human-interpretable concepts from an image and use them for classification. Sparse concept recovery methods leverage the latent space of vision-language models to represent image embeddings as sparse combinations of concept embeddings. However, by ignoring the hierarchical structure of semantic concepts, these methods may produce correct predictions with explanations that are inconsistent with the hierarchy. In this work, we propose Hierarchical Concept Embedding & Pursuit (HCEP), a framework that induces a hierarchy of concept embeddings in the latent space and performs hierarchical sparse coding to recover the concepts present in an image. Given a hierarchy of semantic concepts, we introduce a geometric construction for the corresponding hierarchy of embeddings. Under the assumption that the true concepts form a rooted path in the hierarchy, we derive sufficient conditions for their recovery in the embedding space. We further show that hierarchical sparse coding reliably recovers hierarchical concept embeddings, whereas standard sparse coding fails. Experiments on real-world datasets show that HCEP improves concept precision and recall compared to existing methods while maintaining competitive classification accuracy. Moreover, when the number of samples available for concept estimation and classifier training is limited, HCEP achieves superior classification accuracy and concept recovery. Our results demonstrate that incorporating hierarchical structure into sparse concept recovery leads to more faithful and interpretable image classification models.