Learning from limited or imperfect data (L^2ID) refers to a variety of studies that attempt to address challenging pattern recognition tasks by learning from limited, weak, or noisy supervision. Supervised learning methods including Deep Convolutional Neural Networks have significantly improved the performance in many problems in the field of computer vision, thanks to the rise of large-scale annotated data sets and the advance in computing hardware. However, these supervised learning approaches are notoriously "data hungry", which makes them sometimes not practical in many real-world industrial applications. This issue of availability of large quantities of labeled data becomes even more severe when considering visual classes that require annotation based on expert knowledge (e.g., medical imaging), classes that rarely occur, or object detection and instance segmentation tasks where the labeling requires more effort. To address this problem, many efforts, e.g., weakly supervised learning, few-shot learning, self/semi-supervised, cross-domain few-shot learning, domain adaptation, etc., have been made to improve robustness to this scenario. The goal of this workshop is to bring together researchers to discuss emerging new technologies related to visual learning with limited or imperfectly labeled data. Topics that are of special interest (though submissions are not limited to these):
Please feel free to contact us if you have any suggestions to improve our workshop!    l2idcvpr@gmail.com
Date | Speaker | Topic |
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8:20-8:30 | Organizers | Introduction and opening |
8:30-9:15 | TBA | Invited Talks Topic 1 |
9:15-10:00 | TBA | Spotlight Talks |
10:15-11:00 | TBA | Coffee Break + Poster |
11:00-11:45 | TBA | Invited Talks Topic 2 |
14:00-14:45 | TBA | Invited Talks Topic 3 |
14:45-15:30 | TBA | Invited Talks Topic 4 |
15:30-16:15 | TBA | Invited Talks Topic 5 |
16:15-17:00 | TBA | Track Winners |
17:00-17:15 | TBA | Awards and Wrapup Discussion |
Description | Date |
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Paper submission deadline | March 25th, 2021 |
Notification to authors | April 8th, 2021 |
Camera-ready deadline | April 20th, 2021 |