The AAAI-21 First International Workshop on Meta-Learning for Computer Vision (MeL4CV) will be held at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).

Paper submission deadline: November 9, 2020 November 20, 2020
Decisions to authors : November 30, 2020
Workshop : February 8, 2021

Note: Papers within the scope of the workshop that are rejected from AAAI2021, NeurIPS2020 or other top conferences with a borderline score (e.g. 4.5 or higher at a scale of 10), are encouraged to be submitted along with a supplementary file with details of the comments and authors' response to the comments.

Machine learning and in particular deep learning has shown significant boost in performance on various computer vision tasks such as object recognition, face recognition, and semantic segmentation in the last decade. Despite this success, the learning mechanism of modern systems remains surprisingly narrow as compared to the way humans learn. For example, contrary to the most current systems which learn just a single model from just a single data set, we humans acquire knowledge from diverse experiences over many years. As an alternative, meta-learning and life-long learning aka never-ending learning has been emerging as a new paradigm in the machine learning literature.

Meta learning and lifelong learning relate to the human ability of continuously learning new tasks with very limited labeled training data. In the current computer vision problems, we train one architecture for every individual problem, as soon as the data distribution or the problem statement changes, the machine learning algorithm has to be retrained or redesigned. Further, once the model is updated to incorporate newer data distribution or task, the knowledge learnt from the previous task is “forgotten”. Meta learning focuses on designing models that utilize prior knowledge learnt from other tasks to perform a new task. Meta learning attempts to build models for “general artificial intelligence”. The scope of the workshop includes but are not limited to the following topics:

• Efficient models of meta learning for computer vision
• Lifelong learning for computer vision
• Never-ending multimodal networks for computer vision
• Robust approaches to address catastrophic forgetting
• Imitation learning for visual understanding
• Neural architecture search
• Active Domain Generalization
• Meta Domain Generalization
• Domain-Shift Detection
• Learning to Learn
• AutoML
• Meta-learning Applications in visual domains including biometrics, medical imaging and action recognition.

Paper Submission:
All submissions should follow the AAAI format and should be less than or up to 8 pages excluding references and appendices following AAAI 2021 formatting guidelines. Submissions should be made in PDF format through CMT. Papers will be peer-reviewed and selected for oral or poster presentations at the workshop. At least one author of each accepted submission must be present at the workshop.

The workshop will be organized as a combination of invited keynote presentations and paper presentations. We plan to publish the papers accepted in this workshop as a book with Springer in 2021.