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New benchmarks and frameworks tackle low-light video challenges in computer vision

By PulseAugur Editorial · [2 sources] ·

Two new research papers introduce benchmarks and frameworks for improving computer vision tasks in low-light video conditions. The OLiVES dataset focuses on outdoor low-light videos, providing extensive annotations for both enhancement and segmentation, and is noted as the first of its kind captured with cinema-grade cameras. The ELVIS framework offers a method for adapting existing video instance segmentation models to low-light scenarios by modeling spatial and temporal degradations, showing significant performance improvements on both synthetic and real-world low-light videos. AI

IMPACT These advancements could lead to more robust AI systems capable of understanding and processing visual data in challenging, low-light environments.

RANK_REASON Two academic papers introducing new datasets and frameworks for computer vision tasks.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmarks and frameworks tackle low-light video challenges in computer vision

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yini Li, Joanne Lin, David Blackham, David Bull, Nantheera Anantrasirichai ·

    OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation

    arXiv:2610.20501v1 Announce Type: new Abstract: Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existing public datasets exhibit several limitations, including restricted motion diver…

  2. arXiv cs.CV TIER_1 English(EN) · Joanne Lin, Ruirui Lin, Yini Li, David Bull, Nantheera Anantrasirichai ·

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

    arXiv:2512.01495v4 Announce Type: replace 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 limitatio…

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