NCA-GENM Question 111
Select 3A company is building a multimodal AI system that integrates text, images, and audio data to generate product descriptions for an e-commerce platform. During the development process, the team identifies missing data in several modalities, such as incomplete text descriptions, missing images, and corrupted audio files. Which strategies are most appropriate to address these challenges while ensuring the quality and integration of multimodal data?
- A
Implement imputation techniques to estimate missing data in each modality based on existing patterns.
- B
Discard any data samples with missing or incomplete information to maintain the integrity of the dataset.
- C
Use cross-modal methods to infer missing data from other modalities, such as generating text descriptions from images.
- D
Curate the dataset by manually filling in missing data and removing corrupted files whenever feasible.
- E
Ignore the missing data, as modern deep learning models can inherently handle incomplete multimodal datasets.
Show answer and explanation
Correct answers: A, C, D
Explanation
Effectively addressing missing or incomplete data in multimodal datasets is crucial for building reliable AI systems. Strategies such as imputation, cross-modal inference, and manual curation ensure data quality and integrity without discarding valuable samples. Discarding or ignoring missing data can lead to suboptimal performance or loss of important information, which is why those strategies are not ideal.
- A. Correct.
Imputation techniques are effective for estimating missing data in a single modality by leveraging patterns or statistical properties from the available data. This helps maintain data completeness and quality.
- B. Incorrect.
Discarding samples with missing data can lead to significant data loss, which may negatively impact model training and reduce the diversity of the dataset.
- C. Correct.
Cross-modal methods allow leveraging information from one modality to infer missing data in another. For example, generating textual descriptions from visual features (images) can help address missing text data.
- D. Correct.
Curating the dataset manually ensures higher data quality and allows for the removal of corrupted files, but it can be resource-intensive. It is a valid strategy for addressing missing data in certain cases.
- E. Incorrect.
Ignoring missing data is not recommended because incomplete datasets can significantly degrade the performance of multimodal AI systems.