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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?

A) Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
B) Training separate LSTMs for visual and audio features and averaging their outputs.
C) Using a simple feedforward network to combine the ResNet and Wav2Vec features.
D) Ignoring the audio track and only using the video frames.
E) Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.

2. You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)

A) BLEU score between the generated recipe and a reference recipe.
B) Human evaluation of the generated recipe's clarity, coherence, and accuracy.
C) Inception Score of the input image.
D) Calculating the cosine similarity between the word embeddings of the generated and reference recipes.
E) Precision and recall of the ingredients mentioned in the generated recipe compared to a ground truth ingredient list.

3. You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?

A) Incorporating a Transformer-based architecture, such as a Vision Transformer (ViT) for visual feature extraction and a standard Transformer for sequence generation.
B) Reducing the frame rate of the input video to reduce the temporal complexity.
C) Increasing the number of layers in the CNN to extract more detailed visual features.
D) Using a smaller batch size during training to reduce memory consumption.

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

5. You're using a pre-trained multimodal model that combines visual and textual information for a new downstream task: generating marketing slogans for product images. The model performs poorly, generating generic slogans that are unrelated to the specific product features. What is the MOST effective strategy to adapt this pre-trained model to your specific task?

A) Use the pre-trained model as is, without any adaptation.
B) Only fine-tune the visual encoder component of the pre-trained model.
C) Fine-tune the entire pre-trained model on a dataset of product images and corresponding marketing slogans.
D) Freeze the pre-trained model's weights and train a separate model to map the pre-trained model's output to marketing slogans.
E) Replace the model's output layer with a new layer trained specifically to generate marketing slogans.

Solutions: