W01 –Correspondence Problem in Computer Vision and Pattern Recognition (CPPR 2018)
The recent years have witnessed the significant advancement of techniques for automatic correspondence among visual data. Although visual correspondence has been well studied in multi-view geometry, its generalized forms, and the potential connections with other relevant tasks, are still not fully investigated. Meanwhile, the big data and deep learning paradigm, which has achieved major success in perceptual tasks, has still not been well capitalized for visual correspondence. In this workshop, we attempt to assemble recent advances in the correspondence problem, in an effort for connecting the local and global structures with the modern learning and data processing paradigms.
Junchi Yan; Shuhan Shen; Changsheng Li; Yan Zhu; Yinqiang Zheng; Xiaoyong Pan
Workshop Website: http://vision.ia.ac.cn/CCPRWorkshop2018/index.html
W02 –Computer Vision for Analysis of Underwater Imagery (CVAUI 2018)
The analysis of underwater imagery imposes a series of unique challenges, which need to be tackled by the computer vision community in collaboration with biologists and ocean scientists. We invite submissions from all areas of computer vision and image analysis relevant for, or applied to, underwater image analysis.
Alexandra Branzan Albu; Maia Hoeberechts
Workshop Website: http://cvaui2018.oceannetworks.ca
W03 - Deep Learning for Pattern Recognition (DLPR 2018)
Deep Learning, which can be treated as the most significant breakthrough in the past 10 years in the field of pattern recognition and machine learning, has greatly affected the methodology of related fields like computer vision and achieved terrific progress in both academy and industry. It can be seen as a resolution to change the whole pattern recognition system. It achieved an end-to-end pattern recognition, merging the previous steps of pre-processing, feature extraction, classifier design and post-processing. It is expected that the development of deep learning theories and applications would further influence the field of pattern recognition. The major goal of this workshop is to provide a platform for researchers or graduate students around the world to report or exchange their progresses on deep learning for pattern recognition.
Xiang Bai (Professor, Huazhong University Science and Technology); Yi Fang (Professor, New York University Abu Dhabi and New York University); Yangqing Jia (Research Lead and Manager, Facebook); Meina Kan (Associate Researcher, Institute of Computing Technology, Chinese Academy of Sciences); Shiguang Shan (Researcher, Institute of Computing Technology, Chinese Academy of Sciences); Chunhua Shen (Professor, University of Adelaide); Jingdong Wang (Researcher, Microsoft Research Asia); Gui-Song Xia (Professor, Wuhan University); Shuicheng Yan (Professor, National University of Singapore); Zhaoxiang Zhang (Professor, Institute of Automation, Chinese Academy of Sciences)
Workshop Website: http://valser.org/DLPR/2018.htm
W04 - 3rd International Workshop on Face and Facial Expression Recognition from Real-World Videos (FFER 2018)
The face plays a key role in many real-world applications such as security systems, human computer interaction, remote monitoring of patients, video annotation, and gaming. Having detected the face, pattern recognition techniques and machine learning algorithms are applied to facial images, for example, to find the identity of a subject or analyze her/his emotional status. Though face and facial expression recognition in still images and in ideal imaging conditions have been around for many years, they have been less explored in video sequences in uncontrolled real-world videos. Given the ubiquitous presence of video cameras, face and facial expression recognition from such videos is becoming increasingly important for many applications, for instance for security surveillance, remote patient monitoring. Recognizing faces and facial expressions from real-world videos, however, remain challenging because of low video quality, illumination variation, head pose variation, and significant occlusion. Despite these challenges, video offers dynamics and motion information that is not available in still image and they can be exploited to improve the recognition. The purpose of this workshop is to bring together researchers who are working on developing face and facial expression recognition systems that involve non-ideal conditions, like those that might be present in a real-world video.
Kamal Nasrollahi (Aalborg University, Denmark); Gang Hua (Microsoft Research, USA); Thomas B. Moeslund (Aalborg University, Denmark); Qiang Ji (Rensselaer Polytechnic Institute, USA)
Workshop Website: https://ffer.aau.dk/
W05 - 7th IAPR International Workshop on Computational Forensics (IWCF 2018)
With the advent of high-end technology, fraudulent efforts are on rise in many areas of our daily life, may it be fake paper documents, forgery in the digital domain or copyright infringement. In solving the related criminal cases use of pattern recognition (PR) principles is also gaining an important place because of their ability in successfully assisting the forensic experts to solve many of such cases.
The 7th IAPR International Workshop on Computational Forensics (IWCF) will aim at addressing the theoretical and practical issues related to this field, i.e. role of PR techniques for analysing problems in forensics. Effort is to bring the people together who are working on these issues in different areas including document and speech processing, music analysis, digital security, forensic sciences, etc.
Jean-Marc Ogier (University of La Rochelle, France); Chang-Tsun Li (Charles Sturt University, Australia); Nicolas Sidère (University of La Rochelle, France)
Workshop Website: http://iwcf2018.univ-lr.fr
W06 - ICPR Workshop on Pattern Recognition in Intelligent Financial Analysis and Risk Management (PRIFR 2018)
Recently, financial industry is featured with massive volumes of both structured and unstructured data, which are often associated with unlabeled and partially labeled data, or noisy and uncertain labels. Developing intelligent financial analysis and risk management tools for such data present major challenges for both practitioners and academic researchers. The proposed workshop mainly focuses on pattern recognition and machine learning methods such as kernel methods, feature selection, reinforcement learning, complex networks, deep learning methods, etc. for building intelligence for financial analysis and risk-based knowledge discovery.
Lu Bai (Central University of Finance and Economics, Beijing, China); Jian Tang (HEC Montreal & Montreal Institute for Learning Algorithms (MILA), Canada); Luca Rossi (Department of Computer Science, Aston University, UK); Lixin Cui (Central University of Finance and Economics, Beijing, China)
Workshop Website: https://cs.aston.ac.uk/icprprifr
W07 – 2nd Workshop on Reproducible Research in Pattern Recognition (RRPR 2018)
Following the success of the first workshop Reproducible Research on Pattern Recognition that held at the previous ICPR event 2016, this event will propose a new edition in continuation of the previous event with a new special focus on Digital Geometry and Mathematical Morphology. As for the previous edition, it is intended as both a participative short course on the basis of RR with open discussions with the attendants, and also as a practical workshop on how to do actual RR.
Bertrand Kerautret (main chair); Miguel Colom; Bart Lamiroy; Daniel Lopresti; Pascal Monasse; Jean-Michel Morel; Hugues Talbot
Workshop Website : https://rrpr2018.sciencesconf.org