Workshop submission for main conference paper under review [D]
Summary
An academic is seeking clarification on dual submission policies for a paper currently under review for the ECCV main conference. The query specifically concerns submitting the same work to a non-archival workshop, which is scheduled to occur before the ECCV conference itself but after the final ECCV acceptance or rejection decisions are released. The individual is not the primary author of the ECCV submission and has already submitted an abstract for the workshop, described as a "women event". The central dilemma involves understanding the potential implications of this workshop submission on the ECCV paper's status, irrespective of whether the main conference paper is ultimately accepted or rejected, and navigating the ethical considerations of academic publishing.
Key takeaway
For academics considering submitting work to multiple venues, especially when a paper is under review, you must thoroughly review the dual submission policies of all involved conferences and workshops. Your primary responsibility is to ensure compliance with the main conference's rules, such as ECCV's, and to communicate transparently with all co-authors, particularly the main author, before proceeding with any additional submissions. Verify if the workshop is truly non-archival and if its abstract submission constitutes a "publication" under the main conference's definition.
Key insights
Navigating dual submission policies requires understanding conference rules and co-author agreements.
Principles
- Adhere to conference dual submission policies.
- Communicate with all co-authors on submission plans.
- Distinguish between archival and non-archival venues.
In practice
- Review ECCV's dual submission guidelines.
- Consult with the ECCV paper's main author.
- Verify the workshop's publication policy.
Topics
- Academic Publishing
- Dual Submission Policies
- ECCV Conference
- Non-Archival Workshops
- Publication Ethics
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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.