标签归档:Region Grouping

Interpretable and Accurate Fine-grained Recognition via Region Grouping

Interpretable and Accurate Fine-grained Recognition via Region Grouping

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 finegrained recognition datasets, including CUB-200 [56], CelebA [36] and iNaturalist [55]. 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.

https://arxiv.org/pdf/2005.10411.pdf

本文提出了一种基于图像分割理解的图像识别方法,核心方法是利用region-based的标签训练对应的分类器,从理解部分图像开始,进而理解整幅图像。作者提出的方法在精细图像分割数据集CUB-200,CelebA等上取得了SOTA的成绩。