DeepSeek-VL: Towards Real-World Vision-Language Understanding (2403.05525v2)

Abstract: We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-LLM should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.

DeepSeek-VL: A New Horizon in Vision-LLMs

Introduction

The integration of vision and language understanding has long been a challenging yet critical goal in artificial intelligence research. Vision-LLMs (VLMs) are at the forefront of bridging this gap, enabling machines to comprehend and generate responses based on visual and textual inputs. DeepSeek-VL presents an innovative leap in the development of open-source VLMs, offering a pragmatic approach optimized for real-world applications. Drawing from the strengths of LLMs, DeepSeek-VL introduces a novel methodology to retain linguistic abilities while embracing multimodal data during pretraining. This entry focuses on the distinct strategies employed in DeepSeek-VL’s creation, including data construction, model architecture, training strategies, and a comprehensive evaluation across a range of benchmarks.

Model Architecture

DeepSeek-VL incorporates a hybrid vision encoder that efficiently handles high-resolution images, a crucial aspect of understanding detailed visual information. The model's architecture is designed to process 1024 x 1024 resolution images within a fixed token budget, showcasing an effective balance between capturing essential details and maintaining low computational demands. This architectural choice addresses the challenge of processing complex real-world scenarios, such as fine-grained object recognition and detailed OCR tasks.

Data Construction

The robustness of DeepSeek-VL is significantly attributable to its extensive pretraining data, meticulously curated to cover a wide spectrum of real-world scenarios. This dataset spans from web screenshots, PDFs, and OCR tasks to charts and knowledge-based content, ensuring a broad representation of practical contexts. Additionally, the model benefits from an instruction-tuning dataset specifically designed around real user scenarios, enhancing its relevance and effectiveness in practical applications.

Training Strategy

A key innovation in DeepSeek-VL's development is the strategic approach to training, aimed at preserving the model's language capabilities while incorporating vision and language modalities. The training begins with a significant emphasis on text, gradually adjusting the multimodal ratio to ensure a balanced development of both capabilities. This method effectively prevents the potential degradation of linguistic performance, a common challenge faced by multimodal models.

Evaluation and Implications

DeepSeek-VL has undergone rigorous testing across a broad spectrum of visual-language benchmarks, achieving state-of-the-art or highly competitive performance. The model demonstrates superior capabilities in language understanding, visual comprehension, and multimodal interaction, marking it as a significant contribution to the field. DeepSeek-VL’s performance highlights its potential as a foundational model for a wide range of applications, pushing the boundaries of what is achievable with open-source VLMs.

Limitations and Future Directions

Despite its achievements, DeepSeek-VL has limitations, particularly in scaling the model size and integrating Mixture of Experts (MoE) technology. Future work will focus on overcoming these challenges, with plans to scale up DeepSeek-VL and enhance its efficiency, potentially setting new benchmarks in the VLM landscape.

Conclusion

DeepSeek-VL represents a significant stride towards realizing the full potential of vision-LLMs. By effectively combining deep language understanding with robust visual processing capabilities, DeepSeek-VL sets a new standard for open-source models in real-world applications. Its development strategy, focused on comprehensive pretraining, careful data curation, and a balanced training approach, provides valuable insights for future advancements in VLMs.