NCA-GENM Question 4
Single answerA team is building a multimodal generative AI model to produce captions for images. Which algorithm or technique is most appropriate for training the model to associate text descriptions with images?
- A
Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for text generation
- B
Support Vector Machines (SVMs) for classification and clustering
- C
K-Nearest Neighbors (KNN) for similarity-based learning
- D
Random Forests for decision tree-based predictions
Show answer and explanation
Correct answer: A
Explanation
To generate captions for images in a multimodal AI model, the system must process both visual and textual data. CNNs excel at extracting features from images, and RNNs (or their modern variants like LSTMs or GRUs) are commonly used for generating text sequences. This combination enables the model to learn relationships between images and their corresponding descriptive text.
- A. Correct.
Convolutional Neural Networks (CNNs) are highly effective for processing image data, while Recurrent Neural Networks (RNNs) are commonly used for sequential data like text. Together, they are well-suited for tasks that require associating textual data with visual content, such as generating captions for images.
- B. Incorrect.
Support Vector Machines (SVMs) are designed for classification and regression tasks but are not suitable for the complex multimodal associations required between images and text.
- C. Incorrect.
K-Nearest Neighbors (KNN) is a simple algorithm used for classification and regression based on similarity but lacks the capacity to learn complex relationships between image and text data.
- D. Incorrect.
Random Forests are ensemble learning methods used primarily for structured data and classification problems, and they are not designed for multimodal data or sequential text generation.