ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections
This addresses a specific bottleneck in Vision Transformers for computer vision tasks, offering incremental improvements in accuracy and robustness.
The paper tackles the problem of feature collapse in deeper layers of Vision Transformers, which causes loss of low-level visual features, by proposing a residual attention learning method to enhance feature diversity and robustness. The method achieves improved performance on five image classification benchmarks, including ImageNet1k, and shows effectiveness in object detection and instance segmentation on COCO2017.
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.