  Recent studies indicate that Vision Transformers (ViTs) are robust against
out-of-distribution scenarios. In particular, the Fully Attentional Network
(FAN) - a family of ViT backbones, has achieved state-of-the-art robustness. In
this paper, we revisit the FAN models and improve their pre-training with a
self-emerging token labeling (STL) framework. Our method contains a two-stage
training framework. Specifically, we first train a FAN token labeler (FAN-TL)
to generate semantically meaningful patch token labels, followed by a FAN
student model training stage that uses both the token labels and the original
class label. With the proposed STL framework, our best model based on
FAN-L-Hybrid (77.3M parameters) achieves 84.8% Top-1 accuracy and 42.1% mCE on
ImageNet-1K and ImageNet-C, and sets a new state-of-the-art for ImageNet-A
(46.1%) and ImageNet-R (56.6%) without using extra data, outperforming the
original FAN counterpart by significant margins. The proposed framework also
demonstrates significantly enhanced performance on downstream tasks such as
semantic segmentation, with up to 1.7% improvement in robustness over the
counterpart model. Code is available at https://github.com/NVlabs/STL.
