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Vision Language Models (VLMs)
Vision Language Models (VLMs) integrate Computer Vision (CV) and Natural Language Processing (NLP) to interpret and generate content combining images with words, mimicking human-like understanding.
Recent Developments
Recent models like LLaVA and BLIP-2 use image-text pairs to improve cross-modal alignment. Advancements like LLaVA-Next and Otter-HD focus on enhancing image resolution and token quality within LLMs, addressing computational challenges.
Introduction of Mini-Gemini
Mini-Gemini, developed by the Chinese University of Hong Kong and SmartMore, enhances multi-modal input processing by employing a dual-encoder system, patch info mining, and a high-quality dataset.
Methodology
Mini-Gemini utilizes a dual-encoder system with a convolutional neural network for image processing and patch info mining for detailed visual cue extraction. It is trained on a composite dataset and is compatible with various Large Language Models (LLMs).
Performance
Mini-Gemini showcased leading performance in zero-shot benchmarks, surpassing established models like Gemini Pro and LLaVA-1.5 in various tasks.
Conclusion
Mini-Gemini advances VLMs through its dual-encoder system, patch info mining, and high-quality dataset, outperforming established models and marking a significant step forward in multi-modal AI capabilities.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Mini-Gemini: A Simple and Effective Artificial Intelligence Framework Enhancing multi-modality Vision Language Models (VLMs)
- MarkTechPost
- Twitter – @itinaicom
