Joanne Lin

2 accepted papers

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

Cited by 0 Source ↗code ↗Scholar ↗

2025

Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation

Joanne Lin, Nantheera Anantrasirichai, David Bull

ICASSP 2025accepted

Instance segmentation for low-light imagery remains largely unexplored due to the challenges imposed by such conditions, for example shot noise due to low photon count, color distortions and reduced contrast. In this paper, we propose an end-to-end solution to address this challenging task. Our prop…

Cited by 0 Source ↗Scholar ↗