With the rapid development of artificial intelligence, the integration of visual and language capabilities has led to breakthrough advancements in Visual Language Models (VLMs). These models are designed to simultaneously process and understand visual and textual data, and are widely used in scenarios such as image captioning, visual question answering, optical character recognition, and multimodal content analysis.
VLMs play a crucial role in developing autonomous systems, enhancing human-computer interaction, and creating efficient document processing tools, successfully bridging the gap between these two data modalities. However, there are still many challenges in handling high-resolution visual data and diverse text inputs.
Current research has partially addressed these limitations, but most models utilize static visual encoders that lack adaptability for high-resolution and variable input sizes. Additionally, the combination of pretrained language models and visual encoders often leads to inefficiencies, as they are not optimized for multimodal tasks. Although some models have introduced sparse computation techniques to manage complexity, their accuracy across different datasets remains insufficient. Furthermore, the training datasets of existing models often lack diversity and task specificity, further limiting their performance. For instance, many models struggle with specialized tasks such as chart interpretation or dense document analysis.
Recently, DeepSeek-AI launched the new DeepSeek-VL2 series of open-source Mixture of Experts (MoE) visual language models. This series incorporates cutting-edge innovative technologies, including dynamic slicing for visual encoding, multi-head latent attention mechanisms, and the DeepSeek-MoE framework.
The DeepSeek-VL2 series offers three different parameter configurations:
- DeepSeek-VL2-Tiny: 3.37 billion parameters (1 billion active parameters)
- DeepSeek-VL2-Small: 16.1 billion parameters (2.8 billion active parameters)
- DeepSeek-VL2: 27.5 billion parameters (4.5 billion active parameters)
This scalability ensures its adaptability to various application needs and computational budgets.
The architecture of DeepSeek-VL2 is designed to optimize performance while reducing computational demands. The dynamic slicing approach ensures that high-resolution images are processed without losing critical details, making it well-suited for document analysis and visual localization tasks. Moreover, the multi-head latent attention mechanism enables the model to efficiently handle large volumes of textual data, reducing the computational overhead typically associated with processing dense language inputs. DeepSeek-VL2's training encompasses diverse multimodal datasets, allowing it to excel in various tasks such as optical character recognition, visual question answering, and chart interpretation.
According to performance tests, the Small configuration achieved a 92.3% accuracy rate in optical character recognition tasks, significantly surpassing existing models. In visual localization benchmarks, this model improved precision by 15% compared to its predecessors.
At the same time, DeepSeek-VL2 reduced the demand for computational resources by 30% while maintaining state-of-the-art accuracy. These results demonstrate the model's superiority in processing high-resolution images and text.
Project link: https://huggingface.co/collections/deepseek-ai/deepseek-vl2-675c22accc456d3beb4613ab
Key Points:
🌟 The DeepSeek-VL2 series offers various parameter configurations to meet different application needs.
💡 Dynamic slicing technology enhances the efficiency of high-resolution image processing, suitable for complex document analysis.
🔍 The model excels in optical character recognition and visual localization tasks, showing significant improvements in accuracy.