  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.
