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This paper presents our solution for the 2nd COVID-19 Competition, occurringin the framework of the AIMIA Workshop at the European Conference on ComputerVision (ECCV 2022). In our approach, we employ the winning solution last yearwhich uses a strong 3D Contrastive Mixup Classifcation network (CMC v1) as thebaseline method, composed of contrastive representation learning and mixupclassification. In this paper, we propose CMC v2 by introducing natural videopriors to COVID-19 diagnosis. Specifcally, we adapt a pre-trained (on videodataset) video transformer backbone to COVID-19 detection. Moreover, advancedtraining strategies, including hybrid mixup and cutmix, slicelevelaugmentation, and small resolution training are also utilized to boost therobustness and the generalization ability of the model. Among 14 participatingteams, CMC v2 ranked 1st in the 2nd COVID-19 Competition with an average MacroF1 Score of 89.11%.

CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors