1st International Workshop on Traditional Computer Vision in the Age of Deep Learning (TradiCV) Call for Papers
1st International Workshop on Traditional Computer Vision in the Age
of Deep Learning (TradiCV),
which will be held (virtually) in conjunction with the International
Conference on Computer Vision (ICCV). Selected papers published at the
workshop will be invited to submit an extended version to a Special
Issue on IJCV (stay tuned for details)
All the following information is also available at
https://sites.google.com/view/tradicv
Apologies if you receive this more than once.
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About TradiCV
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In the last 5-10 years we have witnessed that deep learning has
revolutionized Computer Vision, conquering the main scene in most
vision conferences. However, a number of problems and topics for which
deep-learned solutions are currently not preferable over classical
ones exist, that typically involve a strong mathematical model (e.g.,
camera calibration and structure-from-motion).
This workshop concentrates on algorithms and methodologies that
address Computer Vision problems in a “traditional” or
“classic” way, in the sense that analytical/explicit models are
deployed, as opposed to learned/neural ones. A particular focus will
be given to traditional approaches that perform better than neural
ones (for instance, in terms of generalization across different
domains) or that, although performing sub-par, provide clear
advantages with respect to deep learning solutions (for instance, in
terms of efforts to collect data, computational requirements, power
consumption or model compactness).
This workshop also encourages critical discussions about preferring a
traditional solution rather than a deep learning approach and also
explores relevant questions about how to bridge the gap between
learning and classic knowledge. We also expect an insightful
discussion about ethical implications of traditional vision in
comparison to deep learning approaches.
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Call for Papers
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All the Computer Vision topics are welcome as long as the proposed
method does not solely consist in training an end-to-end neural
model. Neural networks are not excluded, though: the workshop welcomes
learning solutions that exploit traditional computer vision as their
core or hybrid approaches that combine deep networks with classical
pipelines.
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Submission
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Two types of submissions are allowed:
REGULAR PAPERS are meant for original, unpublished works. These
papers will be limited to 8 pages (excluding references) and
should follow the ICCV guidelines, including the double blind
policy. These papers will undergo peer-reviewing and will be
published in the workshop proceedings in case of
acceptance. Papers will be selected based on the relevance to the
workshop, significance and novelty of results, technical merit,
and clarity of presentation.
PRESENTATION PAPERS are meant for papers that have been already
accepted at the main conference. These papers will undergo a soft
reviewing process by the chairs to assess their relevance to the
workshop and won't be included in the workshop proceedings.
Submission site: https://cmt3.research.microsoft.com/TradiCV2021
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Important Dates
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Submission opening: ~ Early July, 2021
Paper submission deadline: July 26, 2021
Notification of acceptance: August 10, 2021
Camera-ready deadline: August 17, 2021
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Invited Speakers
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Hongdong Li, Australian National University
Venu Madhav Govindu, Indian Institute of Science
Kathlén Kohn, KTH Stockholm
David Suter, Edith Cowan University
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Organizing Team
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Matteo Poggi, University of Bologna, Italy
Federica Arrigoni, University of Trento, Italy
Andrea Fusiello, University of Udine, Italy
Stefano Mattoccia, University of Bologna, Italy
Adrien Bartoli, Université Clermont Auvergne, France
Torsten Sattler, Czech Technical University in Prague, Czech Republic
Tomas Pajdla, Czech Technical University in Prague, Czech Republic