2014/06/24 by Volodymyr Mnih, Nicolas Heess, Mnih, Volodymyr +5 · 1 voice · 83 citations
Computer Science · Mathematics · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Visual Attention and Saliency Detection #Visual perception and processing mechanisms #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1406.6247
arxiv created 2014/06/24 · openalex publication_date 2014/06/24 · arxiv published 2014/06/24 · arxiv updated 2014/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it performs can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using reinforcement learning methods to learn task-specific policies. We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so.