Call for Papers
Submission deadline extended to April 9th (23:59 PT).
DeepVision: Deep Learning in Computer Vision-->
Description of the workshop
Most of the major advances in Deep Learning have come from supervised learning. Despite these successes, supervised learning algorithms are characterized by a major limitation: they necessitate massive amounts of carefully, and typically expensively, annotated data. This workshop will emphasis future directions beyond supervised learning such as reinforcement learning and weakly supervised learning. Such approaches require far less supervision and allow computers to learn beyond mimicking what is explicitly encoded in a large-scale set of annotations. 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:
- Large scale image and video understanding with limited annotations:
- Video classification
- Object recognition
- Object tracking
- Scene understanding
- Industrial and medical applications
- Theoretical foundations of unsupervised learning.
- Unsupervised feature learning and feature selection.
- Deep learning in mobile platforms and embedded systems.
- Advancements in semi-supervised learning and transfer learning algorithms.
- Inference and optimization.
- Applications of unsupervised learning.
- Deep learning for robotics.
- Lifelong learning.
- Reinforcement learning.
As main difference with previous years, for this edition of the workshop, papers are meant to be extended abstracts showing current / preliminary / novel results to encourage discussion during the workshop.
- Kevin Murphy, (Google, USA)
- Vittorio Ferrari, (Google, Switzerland)
- Olga Russakovsky, (Princeton, USA)
- Josef Sivic, (INRIA, France)
- Chris Re, (Stanford, USA)
- Devi Parikh, (Georgia Tech and Facebook AI, USA)
- Adriana Romero, (Facebook AI, USA)
- The submission site is https://cmt.research.microsoft.com/DV2018
- The maximum extended abstract length is 2 pages (excluding references) using the CVPR main conference format.
- Submissions will be rejected without review if they contain more than 2 pages or violate the double-blind policy.
- Papers will not be published (included in proceedings or CVF). There is no intention to ask for long versions of the paper if abstracts are accepted.
- Paper Submission: April 9th, 2018
- Supplemental Material Submission: April 12th, 2018
- Author Notification: May 10th, 2018
- Camera Ready: May 30th, 2017