3 citations · 8 across the 8 of their papers we have counts for
9 papers
Thermo-VL: Extending Vision-Language Models to Thermal Infrared Perception
Rusiru Thushara, Yasiru Ranasinghe, Jay Paranjape +1
Vision-language models (VLMs) often fail under low illumination because their visual grounding is learned predominantly from RGB imagery, whereas thermal infrared preserves complem…
Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
Yilmaz Korkmaz, Vishal M. Patel
MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes more severe under high accelerat…
UniRes: Universal Image Restoration for Complex Degradations
Mo Zhou, Keren Ye, Mauricio Delbracio +3
Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improv…
Reference-Guided Identity Preserving Face Restoration
Mo Zhou, Keren Ye, Viraj Shah +5
Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods o…
The Power of Context: How Multimodality Improves Image Super-Resolution
Kangfu Mei, Hossein Talebi, Mojtaba Ardakani +3
Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inp…
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio +3
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have s…
Latent Feature-Guided Diffusion Models for Shadow Removal
Kangfu Mei, Luis Figueroa, Zhe Lin +3
Recovering textures under shadows has remained a challenging problem due to the difficulty of inferring shadow-free scenes from shadow images. In this paper, we propose the use of…
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
Kangfu Mei, Mauricio Delbracio, Hossein Talebi +3
Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement, restoration,…
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit +4
Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in designing models…