Call for Papers: Special Issue on Deep Learning for Video Analysis and Compression

Guest Editors
Dong Xu (lead guest editor)
University of Sydney, Australia
dong.xu@sydney.edu.au

Rama Chellappa
University of Maryland, College Park, USA
rama@umiacs.umd.edu

Luc Van Gool
ETH Zurich, Switzerland
vangool@vision.ee.ethz.ch

Guo Lu
Beijing Institute of Technology, China
sdluguo@gmail.com

Aims and Scope

Due to the rapid popularization of digital cameras and mobile phone cameras, there is an increasing research interest in developing next-generation technologies for storing, transmitting, indexing and understanding various types of videos including movies, surveillance videos, web videos and personal videos. Deep learning technologies have demonstrated excellent performance in a broad range of video content analysis tasks such as activity recognition and video event recognition, video-based biometrics, video captioning, video question and answering, as well as video super-resolution. Meanwhile, deep video compression has become a new research direction in visual data compression, and recent deep video compression technologies have achieved promising results on benchmark datasets. In some real-world applications, the two tasks (i.e., video compression and video analysis) are tightly coupled with each other. For example, in intelligent video surveillance systems, the videos are often compressed and transmitted back to the servers before performing video content analysis on the server side, and the quality of reconstructed videos will significantly affect the performance of subsequent video analysis algorithms. To this end, it is therefore beneficial to develop advanced deep learning approaches for the new task of joint video content analysis and compression.
This special issue seeks high-quality papers on deep learning for the video analysis/compression applications. The goals of this special issue are three-fold: (1) investing fundamental theories and advanced frameworks for deep video analysis/compression; (2) presenting novel deep learning techniques applicable to at least one existing video analysis/compression application; (3) exploring new research directions (e.g., video compression for machines) for joint video content analysis and compression.

Topics of Interest
Manuscripts addressing a wide range of topics on deep video analysis and compression, including but not limited to the following are solicited:

  • Fundamental theories and frameworks for deep video analysis/compression
  • Deep learning for activity recognition and video event recognition
  • Deep learning for object localization and segmentation in videos
  • Deep learning for video tracking
  • Deep learning for video based biometrics
  • Deep learning for video forensics
  • Deep learning for video and language
  • Deep learning for video super-resolution/denoising/deblurring
  • Deep learning for video compression and restoration
  • Deep learning for joint video content analysis and compression

Submission Guidelines

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guide lines available on the IJCV website at: https://www.springer.com/11263 .

Please select "Deep Learning for Video Analysis and Compression at the beginning of the submission process. Papers submitted to this special issue should have a distinctive title using the format: SI-DLVAC <title>. All papers including invited papers will be peer reviewed by experts in the field.

Important Dates 
Manuscript submission: 15 December 2020
Preliminary results: 15 March 2021
Revisions due: 15 June 202
Notification: 15th August 202
Final manuscripts due: 15 September 2021
Anticipated publication: 4th quarter 2021

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  

All papers will be reviewed following standard reviewing procedures for the Journal. 

Papers must be prepared in accordance with the Journal guidelines: www.springer.com/11263

Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQs,  Tutorials  along with  Help and Support.

Other links include:

Biographies of the Guest Editors:
Dong Xu is Chair in Computer Engineering at the School of Electrical and Information Engineering, The University of Sydney, Australia. After receiving his PhD degree in 2005, he worked as a postdoctoral research scientist at Columbia University from 2006 to 2007 and a faculty member at Nanyang Technological University from 2007 to 2015. He has published more than 100 papers in IEEE Transactions and top tier conferences, among which two of his co-authored works (with his former PhD students) won the prestigious IEEE T-MM 2014 Prize Paper Award and CVPR 2010 Best Student Paper Award. His publications have received over 18,000 citations in Google Scholar. He was selected as the Clarivate Analytics Highly Cited Researcher in the field of Engineering in 2018 and awarded the IEEE Computational Intelligence Society Outstanding Early Career Award in 2017. He is/was on the editorial boards of T-IP (2017-present), T-MM (2016-2018), T-CSVT (2016-2017), T-PAMI (2014-2019) and T-NNLS (2013-2017), as well as other four journals. He also served as a guest editor of ten special issues in IJCV, T-NNLS, T-CSVT, T-CYB, IEEE Multimedia, ACM TOMM, CVIU and other journals. He served as an area chair of AAAI 2020, ICCV 2017, ACM MM 2017, ECCV 2016 and CVPR 2012, as well as a track chair of ICPR 2016. He received the Best Associate Editor Award of T-CSVT in 2017. He is a Fellow of the IEEE.
Rama Chellappa received the MSEE and PhD degrees in electrical engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. During 1981-1991, he was a faculty member in the department of EE-Systems at University of Southern California (USC). During 1991-2020, he has been a professor of electrical and computer engineering (ECE) and an affiliate professor of computer science at the University of Maryland (UMD), College Park, with affiliations in the Center for Automation Research and the Institute for Advanced Computer Studies (Permanent Member). Since August 2020, he is a Bloomberg Distinguished Professor at Johns Hopkins University with joint tenured appointments in the departments of Electrical and Computer Engineering and Biomedical Engineering. His current research areas include image/video processing, computer vision, machine learning, artificial intelligence, and pattern recognition. He has received numerous awards from IEEE, IAPR, UMD, IBM and USC. Recently, he received the 2020 IEEE Kilby Medal for Signal Processing. He served as the editor-in-chief of IEEE Transactions on Pattern Analysis and Machine Intelligence and as the General and Technical Program chair/co-chair for several IEEE international and national conferences and workshops. He is a golden core member of the IEEE Computer Society, served as a distinguished lecturer of the IEEE Signal Processing Society and as the president of IEEE Biometrics Council. He is a Fellow of the IEEE, IAPR, OSA, AAAS, AAAI and ACM and holds six patents.
Luc Van Gool received the degree in electromechanical engineering from the Katholieke Universiteit Leuven, in 1981. Currently, he is a professor with the Katholieke Universiteit Leuven in Belgium and the ETH in Zurich, Switzerland. He leads computer vision research at both places, where he also teaches computer vision. He has authored more than 200 papers in this field. He has been a program committee member of several major computer vision conferences. His main interests include 3D reconstruction and modeling, object recognition,
tracking, and gesture analysis. He received several Best Paper awards. He is a co-founder of 5 spin-off companies. He is a member of the IEEE.
Guo Lu received his PhD degree from Shanghai Jiao Tong University in 2020 and the B.S. degree from Ocean University of China in 2014. He was a visiting student in University of Sydney from 2017 to 2019. Currently, he is an assistant professor with the School of Computer Science, Beijing Institute of Technology, China. His research interests include image and video processing, video compression and computer vision. His works have been published in top-tier journals and conferences (e.g., T-PAMI, T-IP, CVPR and ECCV).