6 papers
P-Flow: Proxy-gradient Flows for Linear Inverse Problems
Zehua Jiang, Fenghao Zhu, Xinquan Wang +2
Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster sampling compared to diffusion models…
One-Step Generative Channel Estimation via Average Velocity Field
Zehua Jiang, Fenghao Zhu, Siming Jiang +5
Generative models have shown immense potential for wireless communication by learning complex channel data distributions. However, the iterative denoising process associated with t…
DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
Bohao Wang, Zehua Jiang, Zhenyu Yang +9
Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limi…
SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality
Chenyang Lei, Liyi Chen, Jun Cen +5
Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many d…
SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality
Chenyang Lei, Liyi Chen, Jun Cen +6
Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many d…
Robust Depth Enhancement via Polarization Prompt Fusion Tuning
Kei Ikemura, Yiming Huang, Felix Heide +3
Existing depth sensors are imperfect and may provide inaccurate depth values in challenging scenarios, such as in the presence of transparent or reflective objects. In this work, w…