You are tasked with building a system that generates realistic images based on both textual descriptions and a semantic segmentation map. The segmentation map provides spatial information about the objects present in the scene. Which of the following generative architectures is MOST appropriate for this multimodal task?

• A.Vanilla Generative Adversarial Network (GAN)
• B.Variational Autoencoder (VAE)
• C.Conditional Generative Adversarial Network (cGAN) with both text and segmentation map as conditions.
• D.Autoregressive model like PixelCNN
• E.Diffusion model without conditioning

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When using prompt engineering with text-to-image models, which of the following techniques are most effective in improving the fidelity and relevance of generated images to the input text?

• A.Using vague and open-ended prompts to encourage creative variations.
• B.Using highly specific and detailed prompts, including attributes, style, and composition.
• C.Using negative prompts to explicitly exclude undesirable elements from the generated image.
• D.Focusing solely on the main subject of the image, omitting any contextual details.
• E.Using a combination of highly specific prompts and negative prompts.

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During data analysis for a multimodal A1 project involving image and text data, you discover that the image dataset contains a large number of blurry or low-resolution images. The text data, however, is relatively clean and well-structured. What is the BEST approach to mitigate the impact of the noisy image data on the overall model performance?

• A.Discard the blurry and low-resolution images from the dataset to ensure data quality.
• B.Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.
• C.Increase the weight of the text data during model training to compensate for the noisy image data.
• D.Train the model on the noisy image data without any preprocessing or data augmentation.
• E.Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.

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You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?

• A.Vanilla GAN
• B.Conditional GAN (cGAN)
• C.CycleGAN
• D.Deep Convolutional GAN (DCGAN)
• E.Super-Resolution GAN (SRGAN)

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You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

• A.Treat all modalitites with equal weights during model training, ignoring potential skewness issues.
• B.Balance the dataset by oversampling under-represented data points within each modality independently.
• C.Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.
• D.Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.
• E.Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.

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