Attention Augmented Convolutional Networks Lyrn.AI

Convolutional neural networks have proven to be a powerful tool for image recognition, allowing for ever-improving results in image classification (ImageNet), object detection (COCO), and other tasks. Despite their success, convolutions are limited by their locality, i.e. their inability to consider relations between different areas of an image. On the other hand, a popular mechanism which has proven success in overcoming locality is self-attention, which has shown to be able to capture long-range interactions (e.g. In a recent paper, Attention Augmented Convolutional Networks (AACN), a team from Google Brain presents a new way to add self-attention to common Computer Vision algorithms. By combining convolutional layers and self-attention layers in a ResNet architecture, the researchers were able to achieve top results in image classification and object detection while requiring a smaller model than non-attention ResNet models.