  Vision Transformer (ViT) self-attention mechanism is characterized by feature
collapse in deeper layers, resulting in the vanishing of low-level visual
features. However, such features can be helpful to accurately represent and
identify elements within an image and increase the accuracy and robustness of
vision-based recognition systems. Following this rationale, we propose a novel
residual attention learning method for improving ViT-based architectures,
increasing their visual feature diversity and model robustness. In this way,
the proposed network can capture and preserve significant low-level features,
providing more details about the elements within the scene being analyzed. The
effectiveness and robustness of the presented method are evaluated on five
image classification benchmarks, including ImageNet1k, CIFAR10, CIFAR100,
Oxford Flowers-102, and Oxford-IIIT Pet, achieving improved performances.
Additionally, experiments on the COCO2017 dataset show that the devised
approach discovers and incorporates semantic and spatial relationships for
object detection and instance segmentation when implemented into spatial-aware
transformer models.
