Ruirui Lin — accepted AI-conference papers

2026

Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

Guoxi Huang, Qirui Yang, Ruirui Lin, Zipeng Qi, David Bull, Nantheera Anantrasirichai

AAAI 2026technical

In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography conditions. This naturally results in a one-to-many mapping problem. To address this, we propose a Bayesian Enhancement Model

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2026

ELVIS: Enhance Low-Light for Video Instance Segmentation in the Dark

Joanne Lin, Ruirui Lin, Yini Li, David Bull, Nantheera Anantrasirichai

CVPR 2026

Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other adverse conditions. The lack of large-scale annotated datasets and the limitations of current synthetic pipelines, particularly in modeling temporal deg

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