Precognition: Seeing through the Future
Topics of the workshop
Vision-based detection and recognition studies have been recently achieving highly accurate performance and were able to bridge the gap between research and real-world applications. Beyond these well-explored detection and recognition capabilities of modern algorithms, vision-based forecasting will likely be one of the next big research topics in the field of computer vision. Vision-based prediction is one of the critical capabilities of humans, and potential success of automatic vision-based forecasting will empower and unlock human-like capabilities in machines and robots.
One important application is in autonomous driving technologies, where vision-based understanding of a traffic scene and prediction of movement of traffic actors is a critical piece of the autonomous puzzle. Various sensors such as camera and lidar are used as "eyes" of a vehicle, and advanced vision-based algorithms are required to allow safe and effective driving. Another area where vision-based prediction is used is medical domain, allowing deep understanding and prediction of future medical conditions of patients. However, despite its potential and relevance for real-world applications, visual forecasting or precognition has not been in the focus of new theoretical studies and practical applications as much as detection and recognition problems.
Through organization of this workshop we aim to facilitate further discussion and interest within the research community regarding this nascent topic. This workshop will discuss recent approaches and research trends not only in anticipating human behavior from videos but also precognition in multiple other visual applications, such as: medical imaging, health-care, human face aging prediction, early even prediction, autonomous driving forecasting, etc.
In this workshop, the topics of interest include, but are not limited to:
- Early event prediction
- Activity forecasting
- Multi-agent forecasting
- Human behavior prediction
- Human face aging prediction
- Anticipation of trajectories
- Short- and long-term prediction and diagnoses in medical imaging
- Predicting frames and features in videos and other sensors in autonomous driving
- Databases, evaluation and benchmarking in precognition.
Workshop paper submission deadline: March 24th, 2019.
Notification to authors: March 31th, 2019
Camera ready deadline: April 7th, 2019
All submitted work will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. For each accepted submission, at least one author must attend the workshop and present the paper. There are two ways to contribute submissions to the workshop:
- Extended abstracts submissions are single-blind peer-reviewed, and author names and affiliations should be listed. Extended abstract submissions are limited to a total of four pages. Accepted abstracts will be presented at the poster session, and will not be included in the printed proceedings of the workshop.
- Full paper submissions are double-blind peer-reviewed. The submissions are limited to a total of eight pages, including all content and references, must be in PDF format, and formatted according to the CVPR style (additional information about formatting and style files is available here). Accepted papers will be presented at the poster session, with selected papers also being presented in an oral session. All accepted papers will be published by the CVPR in the workshop proceedings.
Submission website: https://easychair.org/conferences/?conf=precognition2019
- Carl Wellington (Perception Lead at Uber ATG)
Presentation Topic: Perception and Prediction in Autonomous Driving at Uber ATG
- John R. Smith (IBM Fellow, Manager of AI Tech for IBM Research AI at IBM T. J. Watson Research Center)
Presentation Topic: TBA
- Sonia Phene (Medical Imaging Team at Google Brain)
Presentation Topic: Predicting Medical Diagnoses in Google
- Ali Kamen (Senior Director of AI for Healthcare, Siemens Healthineers)
Presentation Topic: TBA
Program Committee Members
- Marios Savvides, CMU
- Fernando De La Torre, Facebook AI Research
- Carlos Vallespi-Gonzalez, Uber ATG
- Xiaoming Liu, MSU
- Arun Ross, MSU
- Slobodan Vucetic, Temple University
- Radu Timofte, ETH Zurich
- Namhoon Lee, Oxford
- Henggang Cui, Uber ATG
- Fang-Chieh Chou, Uber ATG
- Thi Hoang Ngan Le, CMU
- Nick Rhinehart, CMU
- Chi Nhan Duong, PDActive, Inc.
- Kha Gia Quach, PDActive, Inc.
- De-An Huang, Stanford
- Wei-Chiu Ma, MIT
- Vladan Radosavljevic, OLX
- Ankit Laddha, Uber ATG