[visionlist] [CFP] Workshop on Learning with Limited Labelled Data for Image and Video Understanding, CVPR22

menna seyam menna.seyam at gmail.com
Fri Jan 21 16:17:36 -04 2022

Workshop on *Learning with Limited Labelled Data for Image and Video
Understanding, (L3D-IVU) *in conjunction with IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), 2022.

*** FULL PAPER SUBMISSION DEADLINE: March 5th 2022 23:59 PST ***

Workshop Website: https://sites.google.com/view/l3d-ivu
CMT3 Submission: https://cmt3.research.microsoft.com/L3DIVU2022


We encourage submissions that are under one of the topics of interest, but
also we welcome other interesting and relevant research for learning with
limited labelled data.


   Few-Shot classification, detection and segmentation in still images and
   video, including objects, actions, and scenes.

   Cross-domain few-shot learning.

   Zero-shot learning.

   Self supervised Learning.

   Weakly supervised learning.

   Semi-supervised learning.

   Transfer Learning.

   Open-set learning.

   New benchmarks.

   Real-world applications discussing the societal Impact of fewshot

Accepted papers will be presented at the poster session, some as orals and
two papers will be awarded as the best paper.

*Submission Guidelines:*


   We accept submissions of *max 8 pages* (excluding references). We
   encourage authors to submit 4 page work as well.

   We accept dual submissions to *CVPR 2022* and *L3D-IVU 2022*.

   Submitted manuscripts should follow the CVPR 2022 paper template


   Submissions will be rejected without review if they:

      Contain more than 8 pages (excluding references).

      Violate the double-blind policy.

      Violate the dual-submission policy for papers with more than 4 pages
      excluding references.


   The accepted papers will be linked at the workshop webpage. It will also
   be in the main conference proceedings if the authors agree (this option is
   valid only for *full-length papers not published at CVPR 2022*)

   Papers will be peer reviewed under double-blind policy, and must be
   submitted online through the CMT submission system.
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