@inproceedings{690b59a8161040a5a3cd759944e85ae5,
title = "Boosting object detection with zero-shot day-night domain adaptation",
abstract = "Detecting objects in low-light scenarios presents a persistent challenge, as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However, the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge, we propose to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Re-visiting Retinex theory in the low-level vision, we first de-sign a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next, an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark, DARK FACE, and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://github.com/ZPDu/DAI-Net.",
keywords = "reflectivity, representation learning, degradation, face recognition, lighting, object detection, detectors",
author = "Zhipeng Du and Miaojing Shi and Jiankang Deng",
year = "2024",
month = sep,
day = "16",
doi = "10.1109/CVPR52733.2024.01204",
language = "English",
isbn = "9798350353013",
series = "Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "12666--12676",
booktitle = "2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
address = "United States",
note = "The Thirty-Fourth IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 ; Conference date: 17-06-2024 Through 21-06-2024",
}