We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task
framework for low-level vision that addresses over 100 sub-tasks across four major categories: …

Building a unified model for general low-level vision tasks holds significant research and
practical value. Current methods encounter several critical issues. Multi-task restoration …

Low-level vision involves a wide spectrum of tasks, including image restoration, enhancement,
stylization, and feature extraction, which differ significantly in both task formulation and …

Image editing has achieved remarkable progress recently. Modern editing models could
already follow complex instructions to manipulate the original content. However, beyond …

Despite the significant progress made by deep models in various image restoration tasks,
existing image restoration networks still face challenges in terms of task generality. An intuitive …

We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal
generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by …

The rapid advancement of educational applications, artistic creation, and AI-generated
content (AIGC) technologies has substantially increased practical requirements for …

While modern visual generation models excel at creating aesthetically pleasing natural
images, they struggle with producing or editing structured visuals like charts, diagrams, and …

Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their
reliance on self-attention's quadratic complexity (O(N^2)) creates a major computational …

Medical diagnostic applications require models that can process multimodal medical inputs (images,
patient histories, lab results) and generate diverse outputs including both textual …