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Multimodal AI Models: Vision, Audio, and Text

Multimodal AI models process and generate multiple data types—text, images, audio, and video—within a single architecture. These models represent a significant advancement beyond text-only LLMs.

Architecture

Multimodal models encode different modalities into a shared representation space. A vision encoder (ViT, CLIP) processes images into embeddings. An audio encoder processes speech and sound. A text tokenizer processes language. All embeddings map to the same space where the LLM processes them.

Training uses paired data: image-caption pairs, video-text pairs, audio-transcription pairs. Contrastive learning (CLIP) aligns different modalities in embedding space. Generative models predict text given images or images given text.

Vision-Language Models

GPT-4V, Claude 3, and Gemini process images alongside text. They can describe images, answer questions about visual content, extract text from images (OCR), and analyze charts and diagrams. Vision capabilities extend to video through frame analysis.

Use cases include document analysis (invoices, receipts, forms), content moderation (image safety checking), visual Q&A, and accessibility (image descriptions for screen readers). Prompt vision models with specific tasks: "Extract all text from this receipt" or "Describe the data trend in this chart."

Audio and Speech

Whisper (OpenAI) transcribes speech to text. Eleven Labs generates realistic speech from text. Multimodal models integrate speech understanding and generation. Audio capabilities enable voice interfaces, transcription, translation, and audio content analysis.

Processing audio requires careful handling of temporal context. Longer audio is chunked into segments. Streaming processing enables real-time transcription. Multi-speaker diarization separates different speakers in recordings.

Video Understanding

Video models process sequences of frames with temporal attention. They understand actions, object tracking, scene transitions, and event timing. Gemini and GPT-4V handle video through sampled frames and temporal reasoning.

Video applications include content moderation, video summarization, surveillance analysis, and automated video description. Frame sampling strategy (uniform, keyframe-based, or adaptive) affects both accuracy and cost.

Multimodal Generation

DALL-E, Midjourney, and Stable Diffusion generate images from text descriptions. Sora generates video from text. These models learn the joint distribution of text and visual data. Prompt engineering for image generation requires different techniques than text prompting.

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