论文标题
部分可观测时空混沌系统的无模型预测
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
论文作者
论文摘要
本文介绍了我们针对第二届Covid-19比赛的解决方案，该竞赛是在欧洲计算机视觉会议（ECCV 2022）的Aimia研讨会框架内举行的。在我们的方法中，我们去年采用了获胜解决方案，该解决方案使用强大的3D对比混合分类网络（CMC V1）作为基线方法，由对比度表示学习和混合分类组成。在本文中，我们通过将自然视频先验引入Covid-19诊断来提出CMC V2。特异性地，我们调整了预先训练的（在视频数据集）视频变压器骨架上，以适应COVID-19检测。此外，还利用了高级培训策略，包括混合混合和cutmix，切片增强和小分辨率培训，以提高模型的鲁棒性和概括能力。在14个参与球队中，CMC V2在第二次Covid-19竞赛中排名第一，平均宏观F1得分为89.11％。
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%.
