NCA-GENM Question 1
Single answerYou are tasked with building a multimodal generative AI model that combines text and image data to predict captions for unseen images. Which of the following algorithms or techniques is most appropriate for training this model effectively?
- A
Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for sequence generation
- B
K-Nearest Neighbors (KNN) for classification and clustering
- C
Support Vector Machines (SVMs) for supervised text and image classification
- D
Reinforcement Learning for unsupervised feature extraction in multimodal data
Show answer and explanation
Correct answer: A
Explanation
For tasks involving multimodal data, such as combining image and text information to generate captions, it is essential to use algorithms that can handle both image feature extraction and sequence generation. Convolutional Neural Networks (CNNs) are excellent for extracting features from image data, while Recurrent Neural Networks (RNNs) are designed for processing sequential data like text. This combination is the most common and effective approach for such tasks.
- A. Correct.
Convolutional Neural Networks (CNNs) are commonly used for feature extraction from image data, while Recurrent Neural Networks (RNNs) are well-suited for sequence generation, such as generating captions based on those features. This makes it the most appropriate choice for the described task.
- B. Incorrect.
K-Nearest Neighbors (KNN) is a simple algorithm used for classification and clustering tasks but is not suitable for feature extraction or sequence generation in multimodal AI systems.
- C. Incorrect.
Support Vector Machines (SVMs) are effective for certain classification tasks but are not designed to handle the complex feature extraction and sequence generation required for multimodal AI models.
- D. Incorrect.
Reinforcement Learning is a powerful technique for sequential decision-making problems but is not typically used for feature extraction or generating captions in multimodal systems.