ReViT: Enhancing Vision Transformers with Attention Residual Connections
for Visual Recognition
ReViT: Enhancing Vision Transformers with Attention Residual Connections
for Visual Recognition
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 …