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cs.CV2026

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

Zhongying Deng, Cheng Tang, Ziyan Huang +124

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…

cs.CV2025

Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark

Yi Xin, Jianjiang Yang, Siqi Luo +10

Pre-trained vision models (PVMs) have demonstrated remarkable adaptability across a wide range of downstream vision tasks, showcasing exceptional performance. However, as these mod…

cs.CV2025

Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding

Yi Xin, Qi Qin, Siqi Luo +29

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 util…

cs.CV2025

Lumina-mGPT 2.0: Stand-Alone AutoRegressive Image Modeling

Yi Xin, Juncheng Yan, Qi Qin +18

We present Lumina-mGPT 2.0, a stand-alone, decoder-only autoregressive model that revisits and revitalizes the autoregressive paradigm for high-quality image generation and beyond.…

cs.CV2025

Resurrect Mask AutoRegressive Modeling for Efficient and Scalable Image Generation

Yi Xin, Le Zhuo, Qi Qin +8

AutoRegressive (AR) models have made notable progress in image generation, with Masked AutoRegressive (MAR) models gaining attention for their efficient parallel decoding. However,…

cs.CV2025

Lumina-Image 2.0: A Unified and Efficient Image Generative Framework

Qi Qin, Le Zhuo, Yi Xin +20

We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built u…