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

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