DLCV 2017 - Special Issue on Deep Learning for Computer Vision

Deep Learning has revolutionized computer vision, significantly pushing state-of-art of computer vision systems in a broad array of high-level tasks, largely outperforming systems relying on hand-crafted representations.
This special issue aims at capturing recent developments in deep learning for computer vision, concentrating on tasks beyond image classification, such as: object detection, semantic segmentation, multi task learning, fine-grained recognition, pose estimation, action recognition, video classification and understanding.
== Topics ==
For this special issue, authors are invited to submit original research papers and high-quality survey articles on topics including, but not limited to:
= Methods =
* Multi-task learning
* Learning from multiple modalities
* Weakly supervised learning
* Visualization and understanding of deep neural networks
* Multi-view and 3-D models
* Model compression and acceleration
= Applications =
* Object localization and detection
* Semantic segmentation
* Motion, surface, and depth estimation
* Pose estimation and action recognition
* Video classification and understanding
* Image processing (denoising, inpainting, texture synthesis)
== Paper Submission ==
Full papers can be submitted via the online submission system for CVIU (http://ees.elsevier.com/cviu). Authors need to select “SI:Deep Learning” when they reach the “Article Type Name” step in the submission process. Manuscript preparation must follow the Guide for Authors. The review process will be single-blind. Submissions that are either low quality or are out of scope may be promptly returned without review. Depending on the volume of submissions, contributing authors might be asked to serve as referees for up to two other submissions.
If a preliminary version of the paper has previously appeared in conference, the submitted version should be substantially enhanced (e.g., more comprehensive experimental evaluation, application to additional datasets, deeper theoretical analysis or insights) and a detailed description of the differences between the submissions is required. We encourage submissions accompanied by software that generates the results claimed in the manuscript.
== Dates ==
* Submission Deadline: April 16, 2016
* First Round of Decisions: October 15, 2016
* Revisions of Submissions: December 15, 2016
* Final Decisions/Manuscript: February 15, 2017
* Estimated Online Publication: April 2017
== Guest Editors ==
* Ross Girshick, Research Scientist, Facebook AI Research, USA
* Iasonas Kokkinos, Associate Professor, CentraleSupélec and INRIA, France
* Ivan Laptev, Research Director, INRIA, France
* Jitendra Malik, Professor, UC Berkeley, USA (senior advising editor)
* George Papandreou, Research Scientist, Google, USA
* Andrea Vedaldi, Associate Professor, Oxford University, UK
* Xiaogang Wang, Assistant Professor, CUHK, Hong Kong
* Shuicheng Yan, Associate Professor, NUS, Sin?ga?pore
* Alan Yuille, Professor, UCLA, USA (senior advising editor)