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NVIDIA Generative AI Multimodal Sample Questions:

1. Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?

A) Cohen's Kappa
B) Pearson correlation coefficient
C) Spearman's rank correlation coefficient
D) Chi-squared test
E) Krippendorff's Alpha

2. You are building a system that takes an image of a scene and a short audio clip as input and generates a descriptive text. You want to evaluate the system's performance. Which of the following evaluation metrics are MOST suitable for assessing both the accuracy and the coherence of the generated descriptions in relation to the input image and audio?

A) CIDEr, SPICE
B) Perplexity, Word Error Rate (WER)
C) BLEU score, CIDEr, SPICE
D) BLEU score, ROUGE score
E) Inception Score (IS), Frechet Inception Distance (FID)

3. You're training a multimodal model for image and text retrieval. Given an image, the model should retrieve the most relevant text description from a database, and vice-vers a. You're using a dual-encoder architecture, where one encoder processes images and the other processes text, projecting them into a shared embedding space. What is the most effective way to train the model to ensure that semantically similar images and texts have close embeddings, while dissimilar ones have distant embeddings?

A) Use a contrastive loss function that minimizes the distance between embeddings of matching image-text pairs and maximizes the distance between embeddings of non-matching pairs. Example: Triplet Loss, InfoNCE.
B) Apply adversarial training to make the embeddings indistinguishable between the two modalities.
C) Use a reconstruction loss that forces the model to reconstruct the input image from its text embedding and vice-versa.
D) Use a simple L1 loss between the image and text embeddings-
E) Train the encoders independently using separate supervised tasks for image and text classification.

4. You are tasked with building a Generative A1 model that can generate realistic images of birds based on text descriptions. You have a large dataset of bird images and corresponding text captions. Which of the following architectures is MOST suitable for this task, considering both image quality and training efficiency?

A) A Variational Autoencoder (VAE) trained on the image dataset.
B) A simple Recurrent Neural Network (RNN) to generate pixel values sequentially.
C) A Generative Adversarial Network (GAN) conditioned on the text descriptions (e.g., a StackGAN or AttnGAN).
D) An Image Transformer model trained from scratch.
E) A standard Convolutional Neural Network (CNN) for image generation.

5. You are working on a Generative A1 Multimodal model that takes text and audio as input and generates a video. During training, you observe that the generated videos often lack coherence with the input text. What are the potential issues you would investigate? (Select THREE)

A) The input audio is too loud.
B) The training dataset does not contain enough diverse examples of text, audio, and video combinations.
C) The discriminator network is too powerful, leading to mode collapse.
D) Lack of a strong conditioning mechanism to guide the video generation based on the input text and audio.
E) Insufficient regularization in the generator network.

Solutions: