Visual attention in deep learning: a review
When facing a complex visual scene, human can efficiently locate region of interest and analyze the scene by selectively processing subsets of visual input. Attention was employed to narrow down the search and speed up the process.1 Visual attention is a hot topic in computer vision, neuroscience and deep learning area. It's widely used in object segmentation, object recognition, image caption generation2,3 and visual question answering (VQA) etc.4 In last few years, deep learning has been growing rapidly. Many Convolutional neural networks and recurrent neural networks have achieved much better performance in various computer vision and natural language processing tasks, compared to previous traditional methods. The visual attention models are mainly categorized into Bottom up models and top down models.
ICRA 2018