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

arXiv: https://arxiv.org/abs/2009.09929 · HTML: https://mlsanity.com/paper/2009.09929/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 26 of 26)

- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding](https://mlsanity.com/paper/2310.15308/) (2023)
- [Continual Learning via Local Module Composition](https://mlsanity.com/paper/2111.07736/) (2021)
- [Recent Advances of Continual Learning in Computer Vision: An Overview](https://mlsanity.com/paper/2109.11369/) (2021)
- [TiC-CLIP: Continual Training of CLIP Models](https://mlsanity.com/paper/2310.16226/) (2023)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation](https://mlsanity.com/paper/2203.08994/) (2022)
- [Continual Learning for Class- and Domain-Incremental Semantic Segmentation](https://mlsanity.com/paper/2209.08023/) (2022)
- [Practical Recommendations for Replay-based Continual Learning Methods](https://mlsanity.com/paper/2203.10317/) (2022)
- [Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition](https://mlsanity.com/paper/2409.16434/) (2024)
- [Balanced softmax cross-entropy for incremental learning with and without memory](https://mlsanity.com/paper/2103.12532/) (2021)
- [Simpler is Better: off-the-shelf Continual Learning Through Pretrained Backbones](https://mlsanity.com/paper/2205.01586/) (2022)
- [Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory](https://mlsanity.com/paper/2309.15413/) (2023)
- [Just a Glimpse: Rethinking Temporal Information for Video Continual Learning](https://mlsanity.com/paper/2305.18418/) (2023)
- [Continual Learning in the Presence of Repetition](https://mlsanity.com/paper/2405.04101/) (2024)
- [Continual Unlearning for Text-to-Image Diffusion Models: A Regularization Perspective](https://mlsanity.com/paper/2511.07970/) (2025)
- [AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models](https://mlsanity.com/paper/2506.09082/) (2025)
- [Future-Proofing Class-Incremental Learning](https://mlsanity.com/paper/2404.03200/) (2024)
- [3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding](https://mlsanity.com/paper/2212.06833/) (2022)
- [Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization](https://mlsanity.com/paper/2403.10894/) (2024)
- [Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning](https://mlsanity.com/paper/2105.14106/) (2021)
- [Understanding Approximation for Bayesian Inference in Neural Networks](https://mlsanity.com/paper/2211.06139/) (2022)
- [Neural Architecture for Online Ensemble Continual Learning](https://mlsanity.com/paper/2211.14963/) (2022)
- [Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning](https://mlsanity.com/paper/2301.02464/) (2023)
- [Realistic Continual Learning Approach using Pre-trained Models](https://mlsanity.com/paper/2404.07729/) (2024)
- [CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments](https://mlsanity.com/paper/2603.17548/) (2026)

## Abstract (arXiv)

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 2300 in prizes. We also summarize the winning approaches, current challenges and future research directions.