The goal of the DeepVision Workshop is to accelerate the study of deep learning algorithms in computer vision problems. With the increase of acceleration of digital photography and the advances in storage devices over the last decade, we have seen explosive growth in the available amount of visual data and equally explosive growth in the computational capacities for image understanding. Instead of hand crafting features, recent advancement in deep learning suggests an emerging approach to extracting useful representations for many computer vision tasks. We encourage researchers to formulate innovative learning theories, feature representations, and end-to-end vision systems based on deep learning. We also encourage new theories and processes for dealing with large scale image datasets through deep learning architectures. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to: Supervised and unsupervised algorithms in computer vision, Deep learning hardware and software architecture, Advancements in deep learning, Large scale computer vision problems including object recognition, scene analysis, industrial and medical applications.