LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions

2026-06-22

AI Summary

This study addresses the challenges of human action recognition under low-light conditions, where insufficient illumination, noise interference, and motion blur severely degrade performance, compounded by the limited diversity and realism of existing datasets. To bridge this gap, the authors introduce LUMINA-26, the first large-scale real-world low-light action recognition dataset comprising 26 action classes and 6,784 videos. They further propose Illumi-Net, a novel illumination-adaptive mixture-of-experts network that integrates video-level illumination cues to jointly perform image enhancement and spatiotemporal feature extraction, enabling accurate recognition through conditional expert routing. The model achieves strong performance on ELLAR (Top-1: 55.13%, Top-5: 78.87%) and establishes a robust baseline on LUMINA-26 (Top-1: 75.95%, Top-5: 93.58%).

Abstract

Low-light human action recognition remains a challenging problem due to poor illumination, amplified noise, motion ambiguity, and diverse real-world scenes. Existing low-light datasets often lack sufficient action diversity, capture realism, or balanced class distribution, limiting the development of robust models. To address this, we introduce LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions, comprising 6,784 clips across 26 action classes, recorded from 22 subjects across 20 indoor and outdoor locations under naturally occurring low-light conditions. We also propose Illumi-Net: An Illumination-Adaptive Mixture-of-Experts Network, which leverages video-level illumination cues to guide adaptive enhancement and transformer-based spatio-temporal feature extraction, with expert-conditioned decision fusion. Our method surpasses previous state-of-the-art performance on ELLAR (Top-1: 55.13%, Top-5: 78.87%) and establishes a strong baseline on LUMINA-26 (Top-1: 75.95%, Top-5: 93.58%), offering a practical benchmark for future low-light action recognition research.

Problem

• low-light
• human action recognition
• illumination
• video understanding
• night-time actions

Innovation

• low-light action recognition
• Illumi-Net
• Mixture-of-Experts
• illumination-adaptive enhancement
• spatio-temporal transformer