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

TL;DR

ELVIS is a framework that enhances low-light video for improved video instance segmentation by modeling degradations and domain adaptation, leading to significant performance gains on synthetic and real low-light videos.

Contribution

The paper introduces ELVIS, a novel domain adaptation framework with an unsupervised synthetic pipeline and degradation estimation for robust low-light video segmentation.

Findings

Up to +3.7 AP improvement on synthetic datasets

At least +2.8 AP better on real low-light videos

Effective modeling of spatial and temporal degradations

Abstract

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 degradations, further hinder progress. Moreover, existing VIS methods are not robust to the degradations found in low-light videos and, consequently, perform poorly even after finetuning. In this paper, we introduce \textbf{ELVIS} (\textbf{E}nhance \textbf{L}ow-Light for \textbf{V}ideo \textbf{I}nstance \textbf{S}egmentation), a framework that enables domain adaptation of state-of-the-art VIS models to low-light scenarios. ELVIS is comprised of an unsupervised synthetic low-light video pipeline that models both spatial and temporal degradations, a calibration-free degradation profile estimation network (VDP-Net) and an enhancement decoder head that disentangles degradations from content features. ELVIS improves performances by up to \textbf{+3.7AP} on the synthetic low-light YouTube-VIS 2019 dataset and beats two-stage baselines by at least \textbf{+2.8AP} on real low-light videos. Code and dataset available at: \href{https://joannelin168.github.io/research/ELVIS}{https://joannelin168.github.io/research/ELVIS}