论文标题
通过区域分组可解释且准确的细粒度识别
Interpretable and Accurate Fine-grained Recognition via Region Grouping
论文作者
论文摘要
我们提出了一个可解释的深层模型，用于细粒度的视觉识别。我们方法的核心是在深神网络中基于区域的零件发现和归因的整合。我们的模型是使用图像级对象标签训练的，并通过分割对象零件及其对分类的贡献来对其结果进行解释。为了促进在没有直接监督的情况下学习对象部分的学习，我们探索了对象部分的简单事先。我们证明，当与我们的基于区域的部分发现和归因结合使用时，这将导致一个可解释的模型，该模型仍然非常准确。我们的模型对包括Cub-200，Celeba和Inaturalist在内的主要细粒识别数据集进行了评估。我们的结果与对分类任务的最新方法相比有利，而我们的方法优于对象零件本地化的先前方法。
We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation of object parts and the identification of their contributions towards classification. To facilitate the learning of object parts without direct supervision, we explore a simple prior of the occurrence of object parts. We demonstrate that this prior, when combined with our region-based part discovery and attribution, leads to an interpretable model that remains highly accurate. Our model is evaluated on major fine-grained recognition datasets, including CUB-200, CelebA and iNaturalist. Our results compare favorably to state-of-the-art methods on classification tasks, and our method outperforms previous approaches on the localization of object parts.
