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

arXiv: https://arxiv.org/abs/2211.14557 · HTML: https://mlsanity.com/paper/2211.14557/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Fixing the train-test resolution discrepancy](https://mlsanity.com/paper/1906.06423/) (2019)
- [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://mlsanity.com/paper/2201.09450/) (2022)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models](https://mlsanity.com/paper/2107.01682/) (2021)
- [Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging](https://mlsanity.com/paper/2206.04732/) (2022)

## Built on by (most cited first, 7 of 7)

- [Advancing COVID-19 Detection in 3D CT Scans](https://mlsanity.com/paper/2403.11953/) (2024)
- [Multi-Source COVID-19 Detection via Variance Risk Extrapolation](https://mlsanity.com/paper/2506.23208/) (2025)
- [Advancing Lung Disease Diagnosis in 3D CT Scans](https://mlsanity.com/paper/2507.00993/) (2025)
- [Domain Adaptation Using Pseudo Labels for COVID-19 Detection](https://mlsanity.com/paper/2403.11498/) (2024)
- [Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization](https://mlsanity.com/paper/2507.19858/) (2025)
- [Clinical Priors Guided Lung Disease Detection in 3D CT Scans](https://mlsanity.com/paper/2603.15143/) (2026)
- [Vision-Language Model Based Multi-Expert Fusion for CT Image Classification](https://mlsanity.com/paper/2603.15154/) (2026)

## Abstract (arXiv)

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%.