CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

by   Vincenzo Lomonaco, et al.

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the significant and undoubted progress of the field in addressing the issue of catastrophic forgetting, benchmarking different continual learning approaches is a difficult task by itself. In fact, given the proliferation of different settings, training and evaluation protocols, metrics and nomenclature, it is often tricky to properly characterize a continual learning algorithm, relate it to other solutions and gauge its real-world applicability. The first Continual Learning in Computer Vision challenge held at CVPR in 2020 has been one of the first opportunities to evaluate different continual learning algorithms on a common hardware with a large set of shared evaluation metrics and 3 different settings based on the realistic CORe50 video benchmark. In this paper, we report the main results of the competition, which counted more than 79 teams registered, 11 finalists and 2300in prizes. We also summarize the winning approaches, current challenges and future research directions.


3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding

Continual Learning, also known as Lifelong or Incremental Learning, has ...

Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Continual learning has received a great deal of attention recently with ...

A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning

Current deep learning research is dominated by benchmark evaluation. A m...

CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability

What is the state of the art in continual machine learning? Although a n...

A Comprehensive Survey of Continual Learning: Theory, Method and Application

To cope with real-world dynamics, an intelligent agent needs to incremen...

Continual Novelty Detection

Novelty Detection methods identify samples that are not representative o...

Continual Learning for Affective Computing

Real-world application require affect perception models to be sensitive ...

Please sign up or login with your details

Forgot password? Click here to reset