DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

DeepSeek-VL2 is an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that address the challenges of high computational costs and limited scalability in large Vision-Language Models (VLMs). It introduces a dynamic, high-resolution vision encoding strategy and an optimized language model architecture that enhances visual understanding and improves training and inference efficiency. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including visual question answering, optical character recognition, document/table/chart understanding, and visual grounding, achieving similar or better performance than state-of-the-art models with fewer activated parameters. The model's efficiency is enabled by its MoE architecture, dynamic tiling approach, and optimized infrastructure choices, making it suitable for deployment in environments with limited computational capacity. DeepSeek-VL2 excels in tasks spanning OCR, document analysis, and visual grounding, showcasing robust multimodal understanding and enhanced instruction-following and conversational skills. Its real-world applicability is demonstrated through its strong performance on quantitative benchmarks and qualitative studies, indicating suitability for practical applications such as automated document processing, virtual assistants, and interactive systems in embodied AI.

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