UniPercept: Unified Perceptual Image Understanding
Analysis
This paper addresses a critical limitation of current Multimodal Large Language Models (MLLMs): their limited ability to understand perceptual-level image features. It introduces a novel framework, UniPercept-Bench, and a baseline model, UniPercept, to improve understanding across aesthetics, quality, structure, and texture. The work's significance lies in defining perceptual-level image understanding in the context of MLLMs and providing a benchmark and baseline for future research. This is important because it moves beyond basic visual tasks to more nuanced understanding, which is crucial for applications like image generation and editing.
Key Takeaways
- •Addresses the limitations of MLLMs in perceptual-level image understanding.
- •Introduces UniPercept-Bench, a unified framework for evaluating perceptual understanding.
- •Develops UniPercept, a strong baseline model.
- •UniPercept outperforms existing MLLMs and can be used as a reward model for image generation.
“UniPercept outperforms existing MLLMs on perceptual-level image understanding and can serve as a plug-and-play reward model for text-to-image generation.”