Deep learning-based object recognition systems can be easily fooled by various adversarial perturbations. One reason for the weak robustness may be that they do not have part-based inductive bias like the human recognition process. Motivated by this, several part-based recognition models have been proposed to improve the adversarial robustness of recognition. However, due to the lack of part annotations, the effectiveness of these methods is only validated on small-scale nonstandard datasets. In this work, we propose PIN++, short for PartImageNet++, a dataset providing high-quality part segmentation annotations for all categories of ImageNet-1K (IN-1K). With these annotations, we build part-based methods directly on the standard IN-1K dataset for robust recognition. Different from previous two-stage part-based models, we propose a Multi-scale Part-supervised Model (MPM), to learn a robust representation with part annotations. Experiments show that MPM yielded better adversarial robustness on the large-scale IN-1K over strong baselines across various attack settings. Furthermore, MPM achieved improved robustness on common corruptions and several out-of-distribution datasets. The dataset, together with these results, enables and encourages researchers to explore the potential of part-based models in more real applications.

翻译：基于深度学习的物体识别系统极易受到各类对抗性扰动的欺骗。其鲁棒性较弱的原因之一可能是这些系统缺乏类似人类识别过程的基于部件的归纳偏置。受此启发，已有若干基于部件的识别模型被提出，旨在提升识别的对抗鲁棒性。然而，由于缺乏部件标注，这些方法的有效性仅在小规模非标准数据集上得到验证。在本工作中，我们提出了 PartImageNet++（简称 PIN++），该数据集为 ImageNet-1K（IN-1K）的所有类别提供了高质量的部件分割标注。利用这些标注，我们直接在标准 IN-1K 数据集上构建了基于部件的方法以进行稳健识别。不同于以往的两阶段基于部件模型，我们提出了一种多尺度部件监督模型（MPM），以利用部件标注学习稳健的表示。实验表明，在各种攻击设置下，MPM 在大规模 IN-1K 数据集上相比强基线模型取得了更好的对抗鲁棒性。此外，MPM 在常见损坏和若干分布外数据集上也实现了鲁棒性的提升。该数据集连同这些结果，将推动并鼓励研究者在更多实际应用中探索基于部件模型的潜力。