Date of publication：2021-07-08 Number of clicks: 10
In June 25, CVPR 2021 launched the 2nd CLVision workshop (IEEE CVPR2021 Workshop on Continual Learning in Computer Vision, website: https://sites.google.com/view/clvision2021/overview), which discusses the recent progress, current limitations, and future directions of continual/incremental learning in computer vision. The workshop received a total of 46 submissions from MIT, Imperial College London, DeepMind etc., which revolve around tasks, methods and libraries of continual/incremental learning. Chen He in our lab won the Best Paper Award as the first author out of the 46 submissions.
Information of the paper：
Chen He, Ruiping Wang, Xilin Chen, “A Tale of Two CILs: The Connections Between Class Incremental Learning and Class Imbalanced Learning, and Beyond,” IEEE CVPR 2021 Workshop on Continual Learning in Computer Vision (CLVision), pp. 3559–3569, June 19-25, 2021.
This paper systematically shows the connections between Class Incremental Learning and Class Imbalanced Learning. Specifically, it demonstrates that many techniques in Class Incremental Learning share similar ideas in Class Imbalanced Learning. By introducing a simple post-scaling technique that originates in Class Imbalanced Learning, the performance is on par or even higher than SOTAs in Class Incremental Learning. By leveraging visualization tools, the paper finds that post-scaling and another effective “weight aligning” technique translates and rotates the decision boundary to alleviate the biasing problem. Based on the theoretical analyses and experimental results mentioned above, the paper reflects upon the recent progress of Class Incremental Learning and raises the question that Class Incremental Learning seems to degenerate into Class Imbalanced Learning. It further provides the authors’ preliminary thoughts on the future directions of this field.
Chen He participated in the workshop and performed a 10-minute oral presentation about the paper to over 100 researchers.
Fig. 1. Certificate of the Best Paper Award
Fig. 2. Oral presentation by Chen He