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"VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction"

Generated below podcast on this paper with Google's Illuminate.

VITA-1.5 sees, listens, and talks back - all in real-time without extra modules.

VITA-1.5 introduces a three-stage training method that integrates vision and speech in LLMs while maintaining strong performance across modalities, enabling real-time multimodal interaction without separate speech processing modules .

→ Matches leading open-source models in vision tasks, surpassing GPT-4V in some benchmarks

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https://arxiv.org/abs/2501.01957

🤔 Original Problem:

Most multimodal LLMs focus only on vision and text, neglecting speech capabilities. Adding speech often degrades vision performance, and current systems require separate speech processing modules that increase latency .

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🔧 Solution in this Paper:

→ A three-stage progressive training strategy gradually incorporates different modalities to prevent interference .

→ Stage 1 focuses on vision-language training through visual adapters and fine-tuning with caption and QA data .

→ Stage 2 introduces audio input processing using speech-transcription pairs and speech QA data .

→ Stage 3 adds end-to-end speech generation without external Text-to-Speech modules .

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💡 Key Insights:

→ Progressive training prevents modality conflicts while maintaining performance

→ End-to-end speech processing reduces latency

→ Single codebook design simplifies speech decoding

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📊 Results:

→ Matches leading open-source models in vision tasks, surpassing GPT-4V in some benchmarks

→ Achieves 2.2% Character Error Rate for Mandarin and 3.3% Word Error Rate for English ASR

→ Maintains vision capabilities after adding speech functionality

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