Please find attached  the Call for Papers for the 

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

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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