Signals 4 · free daily AI digest

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

Paper recorded by Signals 4 on 2026-10-14 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CV · 计算机视觉 · first seen 2026-10-15

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 diversity, limited scene coverage, weak degradations, reliance on auxiliary modalities such as event data, and misalignment between low-light inputs and ground truth. Furthe

Read on arXiv →

#275 most recent of 756 cs.CV papers we have recorded · ↑ newer: Reliability-Aware Multimodal Sentiment Prediction Under Local Modality · ↓ older: Quantifying privacy risks in foundation model representations of brain
Cite this page: OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation: the #275 most recent of 756 cs.CV papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/olives-an-outdoor-low-light-video-benchmark-for-enhancement-and-segmentation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions