TASK 2014 - 2nd Workshop on Transferring and Adapting Source Knowledge in Computer Vision

TASK-CV 2014 - 2nd Workshop on Transferring and Adapting Source Knowledge in Computer Vision
Zürich, 12th September 2014
In conjunction with ECCV 2014
Web: http://www.cvc.uab.es/adas/task-cv2014
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IMPORTANT DATES
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Submission deadline: July 7th, 2014
Author notification: July 21th, 2014
Camera-ready: TBA
Workshop: September 12th, 2014
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CALL FOR PAPERS
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During the first decade of the XXI century, progress in machine learning has had an enormous impact in computer vision. The ability to learn models from data has boosted tasks such as classification, detection, segmentation, recognition, tracking, etc.
A key ingredient of such a success has been the use of visual data with annotations, both for training and testing, and well established protocols for evaluating the results.
However, most of the time, annotating visual information is a tiresome human activity prone to errors. Thus, for addressing new tasks and/or operating in new domains, it is worth it to aspire to reuse the available annotations or the models learned from them.
Therefore, transferring and adapting source knowledge (in the form of annotated data or learned models) has recently emerged as a challenge to develop computer vision methods that are reliable across domains and tasks.
Accordingly, the TASK-CV workshop aims to bring together research in transfer learning (TL) and domain adaptation (DA) for computer vision. We invite the submission of original research contributions such as:
- TL/DA learning methods for challenging paradigms like unsupervised, and incremental or on-line learning.
- TL/DA focusing on specific visual features (HOG, LBP, etc.), models (holistic, DPM, BoW, etc.), or learning algorithms (SVM, AdaBoost, CNN, Random Forest, etc.).
- TL/DA focusing on specific computer vision tasks such as classification, detection, segmentation, recognition, tracking, etc.
- Comparative studies of different TL/DA methods.
- Working frameworks with appropriate CV-oriented datasets and evaluation protocols to assess TL/DA methods.
- Transferring part representations between categories.
- Transferring tasks to new domains.
- Facing domain shift due to sensor differences (e.g., low-vs-high resolution, power spectrum sensitivity) and compression schemes.
- Datasets and protocols for evaluating TL/DA methods.
This is not a closed list; therefore, we welcome other interesting and relevant research on TASK for CV problems.
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INVITED SPEAKERS
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Prof. Kristen Grauman, University of Texas at Austin
Prof. Tinne Tuytelaars, Katholieke Universiteit Leuven
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SUBMISSION
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Submissions should conform to the ECCV 2014 proceedings style. Please follow instructions on the ECCV 2014 website http://eccv2014.org/author-instructions/
Papers must be submitted online through the ECCV 2014 CMT submission system. TASK-CV reviewing will be double-blind. Each submission will be reviewed by at least three reviewers for originality, significance, clarity, soundness, relevance and technical contents.
Submission Deadline: 7th July 2014
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BEST PAPER
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The TASK-CV will award with 400? the best student paper of the workshop, voted by the program committee. More details will be provided in the workshop web page.
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WORKSHOP CHAIRS
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- Antonio M. López, CVC/UAB
- Kate Saenko, UMass Lowell
- Francesco Orabona, TTI Chicago
- José Antonio Rodríguez, XRCE
- David Vázquez, CVC
- Sebastian Ramos, CVC/UAB
- Jiaolong Xu, CVC/UAB
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TECHNICAL PROGRAMME COMMITTEE
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- Yusuf Aytar, University of Oxford
- Barbara Caputo, Idiap Research Institute
- Shih-Fu Chang, Columbia University
- Rama Chellappa, University of Maryland
- Gabriela Csurka, XRC Europe
- Lixin Duan, I2R, Singapore
- Albert Gordo, XRC Europe
- Mehrtash Harandi, NICTA
- Marius Kloft, Courant Institute of Mathematical Sciences
- Christoph Lampert, IST, Austria
- Emilie Morvant, IST, Austria
- Vishal Patel, University of Maryland
- Sam Q. Qiu, Duke University
- Ariadna Quattoni, Universidad Politecnica de Catalunya
- Erik Rodner, Friedrich Schiller University of Jena
- Afshin Rostamizadeh, Google Inc.
- Mathieu Salzmann, NICTA
- Fei Sha, University of Southern California
- Tatiana Tommasi, KU Leuven
- Fernando de la Torre, Carnegie Mellon University
- Ivor Tsang, University of Technology, Sydney
- Dong Xu, Nanyang Technological University