3 citations · 8 across the 8 of their papers we have counts for
8 papers · 1 filter
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…
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…