OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation
We introduce OLiVES, a benchmark dataset for outdoor low-light videos with dynamic scenes, designed to support both low-light video enhancement (LLVE) and video object segmentation (VOS).
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Key points
- Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data.
- The dataset addresses key content gaps by incorporating a diverse range of natural textures, animals, occlusions, dynamic activities, and close-up scenes.
- To the best of our knowledge, OLiVES is the first outdoor low-light video dataset captured using professional cinema-grade cameras.
- We benchmark state-of-the-art LLVE and VOS methods on OLiVES and show that training on it improves performance on both tasks.
Sources (1)
- [1]OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and SegmentationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:08 PM
We introduce OLiVES, a benchmark dataset for outdoor low-light videos with dynamic scenes, designed to support both low-light video enhancement (LLVE) and video object segmentation (VOS).
Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data.
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