• YouTube channel with videos from speakers, orals, posters, and panel sessions is here
• Introduction slides are available here
• The workshop will use the CVPR Zoom link; see the June 20th list of workshops and search for Limited or Imperfect Data. ONLY the poster session at noon PDT will be on Gatherly.
• YouTube video presentations from invited speakers available here! Please ask questions in the comments section and we will ask them during the panels!
• Full list of accepted papers and oral papers available!

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):

• Few-shot learning for image classification, object detection, etc.
• Cross-domain few-shot learning
• Weakly-/semi- supervised learning algorithms
• Zero-shot learning · Learning in the “long-tail” scenario
• Self-supervised learning and unsupervised representation learning
• Learning with noisy data
• Any-shot learning – transitioning between few-shot, mid-shot, and many-shot training
• Optimal data and source selection for effective meta-training with a known or unknown set of target categories
• Data augmentation
• New datasets and metrics to evaluate the benefit of such methods
• Real world applications such as object semantic segmentation/detection/localization, scene parsing, video processing (e.g. action recognition, event detection, and object tracking)

Challenge Information

Workshop Paper Submission Information

• Extended Abstracts of max 4 pages (excluding references)
• Papers of the same length of CVPR submissions