Vision Language Models (VLMs) combine Computer Vision (CV) and Natural Language Processing (NLP) to understand and create content using images and words, similar to human comprehension.
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.
Mini-Gemini, developed by the Chinese University of Hong Kong and SmartMore, enhances multi-modal input processing by using a dual-encoder system, patch info mining, and a high-quality dataset.
Mini-Gemini uses 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).
Mini-Gemini demonstrated leading performance in zero-shot benchmarks, surpassing established models like Gemini Pro and LLaVA-1.5 in various tasks.
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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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
