OPAL让图像分类更可解释，一键定位关键部位并准确分类。
OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification
- 用正交基锚定特征空间，每类用固定部件原型表示。
- 通过空间竞争机制，精准锁定判别性区域，提升定位一致性。
- 单阶段端到端训练，无需额外损失函数，适合需要可解释性的场景。
原型化部件模型通过将输入区域与学习到的原型对比，提供可解释的预测。然而，现有方法依赖复杂的多阶段训练流程，并严重依赖辅助正则化防止原型坍缩。为此，我们提出正交原型对齐学习（OPAL），一种单阶段、端到端的可解释分类框架。该方法使用预定义的正交基锚定潜在空间，将每个类别嵌入由固定部件原型张成的专用子空间中。为实现精确的部件定位，OPAL在特征图上施加空间竞争机制，隔离出稀疏且具有判别性的区域，使每个原型在不同图像中始终关注同一语义概念。通过将分类视为直接表示对齐任务，我们的方法消除了对辅助损失的需求。在细粒度分类基准上的大量实验表明，OPAL优于非可解释的基线模型及当前最先进的部件原型方法，能明确揭示推动每个预测的具体图像区域。代码已开源：https://github.com/ilancarretero/OPAL。
原文摘要 · Abstract (English)
Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burdened by complex, multi-stage training pipelines and heavily rely on auxiliary regularization to prevent prototype collapse. To overcome these limitations, we introduce Orthonormal Prototype Alignment Learning (OPAL), a single-stage, end-to-end framework that simplifies interpretable classification. Our approach anchors the latent space using predefined orthonormal bases, embedding each class within a dedicated subspace spanned by fixed part-prototypes. To achieve precise part localization, OPAL enforces spatial competition across feature maps. This mechanism isolates sparse, discriminative regions, directing each prototype to consistently attend to the same semantic concept across different images. By framing classification as a direct representation alignment task, our method eliminates the need for auxiliary losses. Extensive experiments on fine-grained benchmarks demonstrate that OPAL outperforms both its non-interpretable counterparts and state-of-the-art part-prototype methods, delivering granular visual explanations by explicitly revealing the specific image regions driving every prediction. Code is available at https://github.com/ilancarretero/OPAL.
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