This paper presents our solution for the 2nd COVID-19 Competition, occurring
in the framework of the AIMIA Workshop at the European Conference on Computer
Vision (ECCV 2022). In our approach, we employ the winning solution last year
which uses a strong 3D Contrastive Mixup Classifcation network (CMC v1) as the
baseline method, composed of contrastive representation learning and mixup
classification. In this paper, we propose CMC v2 by introducing natural video
priors to COVID-19 diagnosis. Specifcally, we adapt a pre-trained (on video
dataset) video transformer backbone to COVID-19 detection. Moreover, advanced
training strategies, including hybrid mixup and cutmix, slicelevel
augmentation, and small resolution training are also utilized to boost the
robustness and the generalization ability of the model. Among 14 participating
teams, CMC v2 ranked 1st in the 2nd COVID-19 Competition with an average Macro
F1 Score of 89.11%.

本文介绍了我们的解决方案，使用 3D 对比度混合分类网络（CMC v2）和自然视频优先考虑 COVID-19 诊断，同时利用高级训练策略来提高模型的鲁棒性和泛化能力。在第二届 COVID-19 竞赛中，CMC v2 在 14 支参赛队伍中排名第一，平均宏 F1 得分为 89.11％。