Boosting object detection with zero-shot day-night domain adaptation
Author(s)
Du, Zhipeng
Shit, Miaojing
Deng, Jiankang
Type
Conference Paper
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.
Date Issued
2024-09-16
Date Acceptance
2024-06-17
Citation
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp.12666-12676
ISSN
1063-6919
Publisher
IEEE
Start Page
12666
End Page
12676
Journal / Book Title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Copyright Statement
© 2024 IEEE. This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.
Source
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publication Status
Published
Start Date
2024-06-16
Finish Date
2024-06-22
Coverage Spatial
Seattle, WA, USA