[visionlist] Call for Papers: Multimedia Tools and Applications SI: Few-shot Learning for Multimedia Content UnderstandingPosted: July 30, 2017
The submission deadline is 31. Aug. 2017.
Multimedia Tools and Applications
Special Issue on Few-shot Learning for Multimedia Content Understanding
The multimedia analysis and machine learning communities have long attempted to build models for understanding real-world applications. Driven by the innovations in the architectures of deep convolutional neural network (CNN), tremendous improvements on object recognition and visual understanding have been witnessed in the past few years. However, it should be noticed that the success of current systems relies heavily on a lot of manually labeled noise-free training data, typically several thousand examples for each object class to be learned, like ImageNet. Although it is feasible to build learning systems this way for common categories, recognizing objects “in the wild” is still very challenging. In reality, many objects follow a long-tailed distribution: they do not occur frequently enough to collect and label a large set of representative exemplars in contrast to common objects. For example, in some real-world applications, such as anomalous object detection in a video surveillance scenario, it is difficult to collect sufficient positive samples because they are “anomalous” as defined, and fine-grained object recognition, annotating fine-grained labels requires expertise such that the labeling expense is prohibitively costly.
The expensive labeling cost motivates the researchers to develop learning techniques that utilize only a few noise-free labeled data for model training. Recently, some few-shot learning, including the most challenging task zero-shot learning, approaches have been proposed to reduce the number of necessary labeled samples by transferring knowledge from related data sources. In the view of the promising results reported by these works, it is fully believed that the few-shot learning has strong potential to achieve comparable performance with the sufficient-shot learning techniques and significantly save the labeling efforts. There still remains some important problems. For example, a general theoretical framework for few-shot learning is not established, the generalized few-shot learning which recognizes common and uncommon objects simultaneously is not well investigated, and how to perform online few-shot learning is also an open issue.
The primary goal of this special issue is to invite original contributions reporting the latest advances in few-shot learning for multimedia (e.g., text, video and audio) content understanding towards addressing these challenges, and to provide the opportunity for researchers and product developers to discuss the state-of-the-art and trends of few-shot learning for building intelligent systems. The topics of interest include, but are not limited to:Topics
· Few-shot/zero-shot learning theory;
· Novel machine learning techniques for few-shot/zero-shot learning;
· Generalized few-shot/zero-shot learning;
· Online few-shot/zero-shot learning;
· Few-shot/zero-shot learning with deep CNN;
· Few-shot/zero-shot learning with transfer learning;
· Few-shot/zero-shot learning with noisy data;
· Few-shot learning with actively data annotation (active learning);
· Few-shot/zero-shot learning for fine-grained object recognition;
· Few-shot/zero-shot learning for anomaly detection;
· Few-shot/zero-shot learning for visual feature extraction;
· Applications in object recognition and visual understanding with few-shot learning;
· Manuscript submission deadline: 31 August 2017
· Notification of acceptance: 30 Nov 2017
· Submission of final revised manuscript due: 31 Dec 2017
· Publication of special issue: TBD
All the papers should be full journal length versions and follow the guidelines set out by Multimedia Tools and Applications (http://ift.tt/2eX02b4).
Manuscripts should be submitted online at http://mtap. choosing “1079 – Few-Shot Learning for MM Content Understanding” as article type, no later than 31 August, 2017. All the papers will be peer-reviewed following the MTAP reviewing procedures.
Dr. Guiguang Ding
Affiliation: Tsinghua University, China
Dr. Jungong Han
E-mail: jungong.han@. ac.uk
Affiliation: Northumbria University at Newcastle, UK
Dr. Eric Pauwels
Affiliation: Centrum Wiskunde & Informatica (CWI), Netherlands
We are hiring for several positions in our group within Facebook’s
Building 8 (http://ift.tt/2ixNsxV). It
is an opportunity to work in an exciting area of computer vision
with some top CV researchers on an applied problem. The project is
just starting out, so you will be seeing the project from start to
completion. The positions are relatively short term (~ 1 year)
contract positions. The full description is below. If interested,
send a cover letter and CV to me (Daniel Huber) at firstname.lastname@example.org.
AMFG 2017 – 7th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG)
28 October 2017
Embracing Face and Gesture Analysis in Social Media with Deep Learning
This one-day serial workshop (AMFG2017) will provide a forum for researchers to review the recent progress of recognition, analysis and modeling of face, gesture, and body, and embrace the most advanced deep learning system to address face and gesture analysis particularly under unconstrained environment such as social media. The workshop will consist of one to two invited talks; one from industry, together with peer-reviewed regular papers (oral and poster). Original high-quality contributions are solicited on the following topics:
1. Deep learning methodology, theory, and its applications to social media analytics;
2. Novel deep learning model, deep learning survey, or comparative study for face/gesture recognition;
3. Deep learning for internet-scale soft biometrics and profiling: age, gender, ethnicity, personality, kinship, occupation, beauty, and fashion classification by facial and/or body descriptor;
4. Face, gait, and action recognition in low-quality (blurred for instance), or low-resolution video from fixed or mobile cameras;
5. Novel mathematical modeling and algorithms, sensors and modalities for face & body gesture/action representation, analysis and recognition for cross-domain social media;
6. Deep learning for detection and recognition of face and body in the wild with large 3D rotation, illumination change, partial occlusion, unknown/changing background, and aging; especially large 3D rotation robust face and gesture recognition;
7. Motion analysis, tracking and extraction of face and body models from image sequences captured by mobile devices;
8. Face, gait, and action recognition in low-quality (blurred for instance), or low-resolution video from fixed or mobile cameras;
9. Novel mathematical modeling and algorithms, sensors and modalities for face & body gesture/action representation, analysis, and recognition for cross-domain social media;
10. Social/Psychological studies that can assist in understanding computational modeling and building better automated face and gesture systems for interaction purposes;
11. Novel social applications based on the robust detection, tracking and recognition of face, body, and action;
12. Face and gesture analysis for sentiment analysis in social media;
13. Other applications of face and gesture analysis in social media content understanding.
Submission Deadline: Jul. 31, 2017 [Extended]
Notification: Aug. 15, 2017
Camera Ready: Aug. 20, 2017
Workshop Date: Oct. 28, 2017 (Full day)
Lei Zhang, Microsoft Research
Tim K. Marks, MERL
Xiaoming Liu, MSU
****Honorary General Chair****
Thomas S. Huang, University of Illinois at Urbana-Champaign, USA
Dimitris N. Metaxas, Rutgers, The State University of New Jersey, USA
Yun Raymond Fu, Northeastern University, Boston, USA
Mohammad Soleymani, Swiss Center for Affective Sciences, Switzerland
Ming Shao, University of Massachusetts Dartmouth, USA
Zhangyang (Atlas) Wang, Texas A&M University, USA
****Web and Publicity Co-Chairs****
Zhengming Ding, Northeastern University, Boston, USA
Sheng Li, Northeastern University, Boston, USA
[visionlist] CFP VISIGRAPP 2018 – 13th Int.l Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (Funchal, Madeira/Portugal)Posted: July 28, 2017
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Submission Deadline: July 31, 2017
January 27 – 29, 2018
Funchal, Madeira, Portugal.
In Cooperation with AFIG, Eurographics.
With the presence of internationally distinguished keynote speakers:
Carol O’Sullivan, Trinity College Dublin, Ireland
Alexander Bronshtein, Israel Institute of Technology,Tel Aviv University and Intel Corporation, Israel
Falk Schreiber, University of Konstanz, Germany and Monash University Melbourne, Australia
Catherine Pelachaud, CNRS/University of Pierre and Marie Curie, France
A short list of presented papers will be selected so that revised and extended versions of these papers will be published by Springer.
All papers presented at the congress venue will also be available at the SCITEPRESS Digital Library (http://ift.tt/1iohX1V).
Should you have any question please don’t hesitate contacting me.
Address: Av. D. Manuel I, 27A, 2º esq.
2910-595 Setubal, Portugal
Tel: +351 265 520 184
Fax: +351 265 520 186
I am looking for a postdoc to join my group in the Computer Science Department at Dartmouth College starting in the Fall of 2017. The postdoc will be involved in our research in image and video forensics. The candidate must have finished his or her Ph.D in Computer Science, Computer Engineering, or Electrical Engineering, have significant experience with image processing and analysis, have strong mathematical and computing skills, and ideally have some experience in the field of digital forensics.Dartmouth College is an Ivy League university with graduate programs in the sciences, engineering, medicine, and business. It is located in Hanover, New Hampshire (on the Vermont border) and has a beautiful, historic campus, located in a scenic area on the Connecticut River. Applicants should send a CV and names of three references (letters will be collected at a later date) to: Professor Hany Farid (email@example.com).
The abstract deadline for the 2017 OSA Fall Vision Meeting is tomorrow, July 27th, latest time on earth.
Please submit abstract here:
Remember to add ‘YIA Candidate’ at the end if you want to be considered for the Young Investigator Award.
A tentative schedule and description of the invited talk sessions can be found here:
More information about the meeting is described below.
Hope to see you in DC,
Arthur Shapiro (American University)
Bei Xiao (American University)
The 17th Annual Optical Society Vision Meeting is scheduled to take place at the Katzen Arts Center at American University in Washington, DC, from the 13th to the 15th of October 2017.
Please note the abstract submission deadline is July 27th (Thursday), 2017. The deadline won’t be extended again.
The online registration is here:
The early-bird registration deadline is September 5th, 2017. Information about registration and hotels are available on the meeting website.
This year’s meeting includes five invited sessions, three contributed talk sessions, and a variety of contributed poster presentations. We are also pleased to announce two special events this year: Prof. Ken Nakayama from Harvard University will be presented with the 2017 Tillyer Award for distinguished work in the field of vision; and Prof. David H. Brainard from University of Pennsylvania will present the annual Boynton Lecture.
Attendees of Fall Vision can attend OSA Frontier in Optics (FiO) meeting (Sept 17-21 in DC) complimentary for one day and vice versa attendees of FiO can attend Fall Vision Meeting com
The invited talk sessions include: (1) Applications of High Resolution Retinal Imaging (2) Myopia Development (3) Material Perception (4) From Retina to Extra-striate cortex: Forward Models of Visual Input (5) Lighting, Color Rendering, and Color Vision.
Besides the keynote speakers, the invited speakers includes:
Andrew Pucker, University of Alabama Birmingham
Anya Hurlbert, University of New Castle
Bei Xiao, American University
Brian Wandell, Stanford University
David Huang, Oregon Health and Science University
David Troilo, SUNY College of Optometry
Don Miller, Indiana University
Greg Schwartz, Northwestern University
Ione Fine, University of Washington
Kendrick Kay, University of Minnesota
Lorne Whitehead, University of British Columbia
Machelle Pardue, Georgia Tech
Manuel Spitschan, Stanford University
Mark Fairchild, Rochester Institute of Technology
Michael Landy, New York University
Noah Benson, New York University
Qasim Zaidi, SUNY College of Optometry
Rigmor Baraas, University of Southeast Norway
Shin’ya Nishida, NTT Japan
Stephen Burns, Indiana University