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, which builds on the successful CVPR 2021 L2ID workshop, is to bring together researchers across several computer vision and machine learning communities to navigate the complex landscape of methods that enable moving beyond fully supervised learning towards limited and imperfect label settings. Topics that are of special interest (though submissions are not limited to these):
Description | Date |
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Paper submission deadline | July 15th, 2022 |
Notification to authors | Early August, TBA, 2022 |
Camera-ready deadline | TBA, 2022 |