NCA-GENM Question 26
Select 3You are evaluating the performance of a multimodal AI pipeline designed to analyze video content by combining visual and audio modalities. Which statistical techniques should you use to ensure a thorough analysis of the pipeline's performance across modalities?
- A
Calculate the F1-score separately for each modality and combine the results using a weighted average based on modality importance.
- B
Use correlation analysis to determine the relationship between visual and audio features in the pipeline.
- C
Perform a confusion matrix analysis for individual modalities and cross-modality predictions.
- D
Apply Principal Component Analysis (PCA) to reduce the dimensionality of the combined multimodal feature space.
- E
Evaluate cross-validation results for multimodal models using a single metric like accuracy.
Show answer and explanation
Correct answers: A, B, C
Explanation
Evaluating the performance of a multimodal pipeline requires statistical techniques that account for individual modality performance, cross-modality interactions, and error analysis. F1-scores, correlation analysis, and confusion matrices are all essential for a comprehensive evaluation of such pipelines. PCA and accuracy alone do not provide sufficient insights specific to multimodal systems.
- A. Correct.
Calculating the F1-score separately for each modality and combining the results ensures that each modality's performance is individually understood and that their contributions are appropriately weighted in the final assessment.
- B. Correct.
Correlation analysis helps quantify the relationship and alignment between visual and audio features, which is crucial for understanding how effectively the modalities complement each other.
- C. Correct.
Confusion matrix analysis provides insights into the errors made by the pipeline, both for individual modalities and cross-modality predictions, enabling deeper debugging and optimization.
- D. Incorrect.
While PCA can be helpful for feature reduction, it is not directly a statistical evaluation technique for assessing multimodal pipeline performance.
- E. Incorrect.
Accuracy as a single metric is insufficient for multimodal models because it does not provide insights into individual modality contributions or cross-modality interactions.