SEED-X：具备统一多粒度理解与生成的多模态模型
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
AI总结：
本文提出统一多功能基础模型SEED-X，通过整合任意尺寸图像理解与多粒度图像生成能力，弥合多模态模型与实际应用差距，在公开基准及跨领域实际应用中展现有效性。
摘要：
多模态基础模型的快速发展在视觉语言理解与生成方面展现了显著进展，例如我们之前的工作SEED-LLaMA。然而，其能力与实际应用之间仍存在差距，主要由于模型在有效响应各种用户指令和与多样视觉数据交互方面能力有限。在本工作中，我们致力于通过整合两项增强功能来弥合这一差距：(1) 理解任意大小和比例的图像，以及(2) 实现多粒度图像生成。我们提出了一个统一且多功能的基础模型，即SEED-X，它能够为理解和生成任务建模多粒度视觉语义。除了在公开基准上取得有竞争力的结果外，SEED-X在经过指令微调后，展现了其在处理跨多个领域的实际应用中的有效性。我们希望这项工作能启发未来关于多功能多模态基础模型在实际应用中能够实现什么的研究。模型、代码和数据集已发布在https://github.com/AILab-CVC/SEED-X。
英文摘要：
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 are released in https://github.com/AILab-CVC/SEED-X.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
