Deep Learning to See [electronic resource] : Towards New Foundations of Computer Vision /

1. Introduction -- 2. Cutting the Umbilical Cord with Pattern Recognition -- 3. Spatiotemporal Visual Environments -- 4. Hierarchical Description of Visual Tasks -- 5. Benchmarks and the “En Plein Air” Challenge.

The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this book criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. Specifically, the present work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. This unique volume will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.