CMC_v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors

Abstract

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 Classification 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. Specifically, we adapt a pre-trained (on video dataset) video transformer backbone to COVID-19 detection. Moreover, advanced training strategies, including hybrid mixup and cutmix, slice-level 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%.