The main focus of our workshop is on computer vision tasks that operate at instance-level, including both recognition (instance-level recognition - ILR) and generation (instance-level generation - ILG), denoted as ILR+G. More precisely, ILR+G is the task of identifying, comparing, and generating images of specific objects, scenes, or events. This year, we will organize a call for papers, and host keynote talks by renowned speakers and invited paper talks from the main conference. The 2026 Instance-Level Recognition and Generation (ILR+G) Workshop is a follow-up of seven successful editions of our previous workshops — the first two having focused only on landmark recognition (, ), the following ones expanding to the domains of artworks and products (, ), introducing the universal image embedding problem (, ), and the latest one expanding the scope of our workshop to ILG and the potential synergy between ILG and ILR ().
Adam Harley
Getting to the Point: From Instances to Correspondence
Poster assignments
Sep. 8, 10:05am-11:15am (CEST)
Long Papers
Short Papers
We call for novel and unpublished work in the format of long papers (14 pages excluding references) and short papers (4 pages excluding references). Papers should follow the and will undergo double-blind peer review. Selected long papers will be invited for oral presentations; all accepted papers will be presented as posters. Only long papers will be published in the ECCV workshop proceedings. This year, financial awards will be given to the best papers, recognizing high-quality research with novel and interesting findings, and student support grants will be provided to eligible participants. All submissions will be handled electronically via the .
Topics of interest include (but are not limited to)
Even though tasks such as person and vehicle re-identification fall within the definition of ILR, we intentionally omit them from the list of topics, due to ethical and social implications. Submitted papers on those topics will be desk rejected.
Important Dates
Student Support Grant
To broaden participation, we are offering student support grants to help cover registration costs. Eligible students are encouraged to .
Questions? Please reach out to us at ilr-workshop@googlegroups.com
Google DeepMind
Google DeepMind
xAI
Czech Technical University
The University of Osaka
Google DeepMind
Czech Technical University (Primary Contact)
Czech Technical University
The University of Osaka
Amazon
Czech Technical University
Amazon
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
© 2026 ILR+G 2026
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