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NEW QUESTION # 230
Consider a scenario where you are building a multimodal model that combines image and text data for image captioning. You're using a transformer architecture with cross-attention. Which of the following best describes the role of cross-attention in this context?
Answer: A
Explanation:
Cross-attention in image captioning allows the decoder (generating text) to focus on specific parts of the image that are most relevant for generating the next word in the caption. The text 'attends' to the image.
NEW QUESTION # 231
When evaluating a multimodal generative model, which of the following metrics is MOST suitable for assessing the coherence and consistency between the generated image and its corresponding text description?
Answer: E
Explanation:
The CLIP score directly measures the similarity between image and text embeddings in a joint multimodal space. It's designed to assess how well the generated image aligns with its text description. IS and FID focus solely on image quality. BLEU and perplexity focus solely on text quality. Therefore, only CLIP is a multimodal coherence metric.
NEW QUESTION # 232
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?
Answer: D
Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.
NEW QUESTION # 233
You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?
Answer: E
Explanation:
Negative prompting is a powerful technique where you specify what you don't want in the generated image. This helps refine the output and steer the model away from undesirable artifacts or styles. For example, specifying "a futuristic city, but without flying cars".
NEW QUESTION # 234
Which NVIDIA SDK would be most appropriate for building a real-time, interactive avatar that can respond to voice commands and generate realistic facial expressions?
Answer: C
Explanation:
Avatar Cloud Engine (ACE) is specifically designed for building and deploying interactive avatars. It provides pre-built components and APIs for speech recognition, natural language understanding, and realistic avatar animation. Riva focuses primarily on speech AI, NeMo on training large language models, and Triton on model serving. RAPIDS is for data analytics and GPU-accelerated machine learning.
NEW QUESTION # 235
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