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You are tasked with building a multimodal generative A1 model that takes an image and a text prompt as input and generates a corresponding audio description. The image data is processed with a Vision Transformer (ViT), the text prompt is processed with a Transformer, and you need to fuse these modalities to generate the audio. Which of the following fusion strategies would be MOST appropriate for this task, considering the need for coherent and contextually relevant audio generation?
Correct Answer: B,E
Cross-attention allows the model to selectively focus on the most relevant parts of the image based on the text prompt, enabling it to generate more coherent and contextually relevant audio. Fine-tuning a pretrained text-to-audio model is a strong approach by leveraging existing knowledge of audio generation and guiding it with visual input. Simple concatenation or addition may not capture the complex relationships between modalities. Averaging predictions from separate models doesn't ensure coherence between the image and text. It is better to fine tune existing LLM models or build a fresh model from cross-attention between images and text to predict the final audio.