Papers › Zero-Shot Day-Night Domain Adaptation with a Physics Prior
Zero-Shot Day-Night Domain Adaptation with a Physics Prior
Attila Lengyel, Sourav Garg, Michael Milford, Jan C. van Gemert
We explore the zero-shot setting for day-night domain adaptation. The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set. As gathering relevant test data is expensive and sometimes even impossible, we remove any reliance on test data imagery and instead exploit a visual inductive prior derived from physics-based reflection models for domain adaptation. We cast a number of color invariant edge detectors as trainable layers in a convolutional neural network and evaluate their robustness to illumination changes. We show that the color invariant layer reduces the day-night distribution shift in feature map activations throughout the network. We demonstrate improved performance for zero-shot day to night domain adaptation on both synthetic as well as natural datasets in various tasks, including classification, segmentation and place recognition.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | 24/7 Tokyo | CIConv | mAP | 88.3 | #2 of 3 | Archive leaderboard | report |
| Semantic Segmentation | Dark Zurich | CIConv | mIoU | 34.5 | #14 of 14 | Archive leaderboard | report |
| Semantic Segmentation | Nighttime Driving | CIConv | mIoU | 41.6 | #12 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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