DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding (2412.10302v1)

Abstract: We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-LLMs that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component, we incorporate a dynamic tiling vision encoding strategy designed for processing high-resolution images with different aspect ratios. For the language component, we leverage DeepSeekMoE models with the Multi-head Latent Attention mechanism, which compresses Key-Value cache into latent vectors, to enable efficient inference and high throughput. Trained on an improved vision-language dataset, DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Codes and pre-trained models are publicly accessible at https://github.com/deepseek-ai/DeepSeek-VL2.

DeepSeek-VL2: Advancing Vision-LLMs with Mixture-of-Experts

DeepSeek-VL2 presents a noteworthy improvement over its predecessor DeepSeek-VL, distinguished by its application of Mixture-of-Experts (MoE) architecture to enhance vision-LLMs (VLMs). The research introduces innovative methodologies in processing high-resolution visual data and optimizing language components, providing a holistic enhancement in multimodal understanding tasks.

Key Features and Contributions

The paper outlines significant upgrades in both the visual and language processing elements of VLMs. For the vision encoder, DeepSeek-VL2 implements a dynamic tiling strategy that efficiently accommodates high-resolution images with various aspect ratios. This advancement significantly improves tasks like visual grounding, document analysis, and detailed feature extraction without suffering from the limitations of fixed resolution approaches, which are prevalent in existing models.

In the LLMing domain, DeepSeek-VL2 capitalizes on DeepSeekMoE models leveraging the Multi-head Latent Attention (MLA) mechanism. By compressing the Key-Value cache into latent vectors, the model achieves efficient inference and increased throughput, marking a significant efficiency gain over traditional dense models.

Performance and Implications

DeepSeek-VL2 showcases substantial performance improvements on prominent multimodal benchmarks, including visual question answering, optical character recognition, and chart understanding, achieving competitive or superior results with fewer activated parameters compared to its predecessors and other open-source models. This efficiency is particularly notable across various tasks, implying practical advantages in deployment scenarios where computational resources are constrained.

The implications of the advancements presented are twofold. Practically, the increased efficiency and reduced computational demand make DeepSeek-VL2 a strong candidate for real-world applications that require seamless integration of visual and language data. Theoretically, the research highlights the potential of MoE architectures in addressing the scaling challenges faced by dense models, paving the way for future developments in efficient model design.

Future Directions

While the improvements in multimodal understanding are evident, the paper acknowledges limitations in the current version and highlights potential areas of further research. Extensions of the context window for richer multi-image interactions are planned, as well as enhancing the model’s robustness and reasoning capabilities. Future iterations may also focus on refining the integration of novel vision tasks and expanding the model’s applicability to new domains.

DeepSeek-VL2 stands as a notable advancement in the field of vision-LLMs, underpinning the importance of efficient architectures like Mixture-of-Experts. Its contribution not only advances the current state-of-the-art but also sets the stage for future innovations at the intersection of vision and language processing. As these models evolve, their impact on practical applications will undoubtedly further solidify the role of integrated AI systems in complex, multimodal environments.