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SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation (2404.14396v1)

Published 22 Apr 2024 in cs.CV
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Abstract: The rapid evolution of multimodal foundation model has demonstrated significant progresses in vision-language understanding and generation, e.g., our previous work SEED-LLaMA. However, there remains a gap between its capability and the real-world applicability, primarily due to the model's limited capacity to effectively respond to various user instructions and interact with diverse visual data. In this work, we focus on bridging this gap through integrating two enhanced features: (1) comprehending images of arbitrary sizes and ratios, and (2) enabling multi-granularity image generation. We present a unified and versatile foundation model, namely, SEED-X, which is able to model multi-granularity visual semantics for comprehension and generation tasks. Besides the competitive results on public benchmarks, SEED-X demonstrates its effectiveness in handling real-world applications across various domains after instruction tuning. We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications. The models, codes, and datasets will be released in https://github.com/AILab-CVC/SEED-X.

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Enhancing Multimodal Foundation Models for Real-world Applicability: Introducing SEED-X

Introduction to SEED-X

In the rapidly evolving domain of multimodal foundation models, the transition from laboratory settings to real-world applicability presents notable challenges, primarily due to the models' limited interaction capabilities with diverse visual and instructional data. Addressing these challenges, this paper introduces SEED-X, an enhanced version of the previously developed SEED-LLaMA. SEED-X integrates advanced features to comprehend images of arbitrary sizes and aspect ratios and enables multi-granularity image generation, ranging from high-level instructional creation to detailed image manipulation.

Key Features and Methodology

SEED-X represents a comprehensive approach to multimodal understanding and generation, designed to operate effectively in diverse real-world applications. The model architecture includes significant enhancements over its predecessors:

  • Visual Tokenization and De-tokenization: Utilizes a pre-trained Vision Transformer (ViT) as a visual tokenizer coupled with a visual de-tokenizer that supports the generation of detailed images by interpreting ViT features. This setup helps in accurate image reconstruction aligned with original semantic contexts and detailed image manipulation tasks.
  • Dynamic Resolution Image Encoding: Introduces a method to process images with arbitrary resolutions by employing a grid division technique for image encoding, which preserves detailed information and supports various aspect ratios without requiring standard pre-defined image sizes.
  • Multimodal Pre-training and Instruction Tuning: Employs a large-scale multimodal data corpus for training, followed by instruction tuning to refine the model's capabilities to follow specific instructions in real-world applications, enhancing both comprehension and generation tasks across varied domains.

Evaluation and Performance

Extensive evaluations demonstrate SEED-X's superior performance on several benchmarks designed for multimodal LLMs. It shows competitive results in multimodal comprehension and state-of-the-art performance in image generation tasks compared to existing LLMs. Particularly, SEED-X excels in handling multi-image contexts and generating high-quality, instruction-aligned images.

Implications and Future Prospects

The development of SEED-X marks a significant step toward bridging the gap between academic multimodal model research and practical real-world applications. By enabling nuanced understanding and generation of multimodal data, SEED-X could serve various domains, from creative design to personal assistance and beyond.

Future research could explore further enhancements in the robustness of image tokenization processes and expand the model's adaptability to dynamically varied multimodal scenarios, potentially leading to more generalized AI systems capable of seamless interaction in complex real-world environments.

Conclusion

SEED-X sets a new precedent in the field of multimodal foundation models by substantially enhancing the real-world applicability of such systems. With its robust architecture and superior performance across multiple benchmarks, SEED-X not only fulfills but extends the capabilities expected of next-generation AI models, promising exciting developments in AI applications across industries.

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Authors (9)
  1. Yuying Ge (39 papers)
  2. Sijie Zhao (15 papers)
  3. Jinguo Zhu (20 papers)
  4. Yixiao Ge (99 papers)
  5. Kun Yi (25 papers)
  6. Lin Song (44 papers)
  7. Chen Li (386 papers)
  8. Xiaohan Ding (41 papers)
  9. Ying Shan (252 papers)
Citations (44)
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GitHub

  1. GitHub - AILab-CVC/SEED-X: Multimodal Models in Real World (517 stars)