2024-01-21-OPPO-2023多模态预训练模型在OPPO端云场景的落地实践报告_46页_10mb

Analysis Summary

The provided content describes OPPO's research and practice on multi-modal pretraining models, focusing on end-side (or "edge-side") application of advanced AI technologies. Key points include:

1. Edge-Side Image-Text Retrieval Technology:

   • Purpose: Enhance intelligent search for user queries (e.g., "和女朋3友去迪士尼").
   • Key Features:
     • Natural language query processing for image search.
     • Efficient search speed by deploying large models on devices.
     • Privacy protection by processing search locally.
   • Challenges:
     • Lightweight deployment after model compression.
     • Implementation of an efficient vector retrieval engine.
   • Benefits: Improved efficiency in information search, cost savings, and enhanced user privacy.

2. Implementation and Framework:

   • Use of multi-modal alignment technologies like CLIP and ALBEF.
   • Model optimization techniques such as orthogonal finetuning and multi-teacher distillation.
   • Progressive distillation for faster sampling of diffusion models.

3. Domain-Specific Applications:

   • Person Beauty Vertical: Focus on high-quality fine-tuning and domain-specific adjustments.
   • Text Rendering: Specialized handling for Chinese text rendering in text-to-image models.
   • Personalized Generation: Use of SDD dataset and Subject-Diffusion for personalized image generation.

4. Experimental Results:

   • Performance comparisons of models (e.g., ALBEF vs. CLI
     compact specific text-to-image models).
   • Quantitative data such as precision@k metrics for image-text retrieval.
   • Benchmark results on edge hardware like high-end smartphones (e.g., Snapdragon 8 Gen2).

5. Technical Challenges:

   • Balancing model size reduction and performance.
   • Ensuring fast inference on edge devices by optimizing sampling techniques.

Summary Conclusion

The report explores the application of large language models and multi-modal techniques in on-device scenarios, emphasizing lightweight model deployment and domain-specific optimizations for various applications like image-text retrieval, text-image generation, and personalized content creation. It highlights the trade-offs between model fidelity and deployment efficiency on hardware like Snapdragon-based devices, along with empirical results discussing achievable performance and user benefits.