mlsanityData to 14 Oct 2026

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

arXiv 2211.14557 ↗First posted 26 Nov 2022eess.IV cs.CVcited by 7

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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%.

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

21 of 36 references.

References · 21

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9 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2024Advancing COVID-19 Detection in 3D CT Scanscited by 5
  2. 2025Multi-Source COVID-19 Detection via Variance Risk Extrapolationcited by 5
  3. 2025Advancing Lung Disease Diagnosis in 3D CT Scanscited by 4
  4. 2024Domain Adaptation Using Pseudo Labels for COVID-19 Detectioncited by 3
  5. 2025Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardizationcited by 2
  6. 2026Clinical Priors Guided Lung Disease Detection in 3D CT Scanscited by 0
  7. 2026Vision-Language Model Based Multi-Expert Fusion for CT Image Classificationcited by 0