SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

2024-04-22

arXiv.org

AI Summary

Current multimodal foundation models exhibit notable limitations in visual-language instruction following and diverse image interaction capabilities. To address this, we propose SEED-X—a unified multimodal foundation model that, for the first time, enables joint modeling of understanding and generation tasks across multiple semantic granularities (global–local–fine-grained), supporting uncropped, adaptive-resolution image inputs and hierarchically controllable image generation. Built upon the SEED-LLaMA architecture, SEED-X introduces dynamic resolution encoding, cross-granularity feature alignment, and an end-to-end instruction-tuning framework. It achieves state-of-the-art performance on multiple public benchmarks and demonstrates strong robustness and efficiency on cross-domain real-world tasks—including fine-grained image editing and long visual-text generation—thereby significantly narrowing the gap between model capability and practical deployment.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 are released in https://github.com/AILab-CVC/SEED-X.

Problem

• Bridging gap between model capability and real-world applicability.
• Enhancing image comprehension for arbitrary sizes and ratios.
• Enabling multi-granularity image generation for diverse visual data.

Innovation

• Unified model for multi-granularity comprehension and generation
• Handles images of arbitrary sizes and ratios
• Effective in real-world applications after instruction tuning