Zero-Shot Day–Night Domain Adaptation for Face Detection Based on DAl-CLIP-Dino

Two challenges in computer vision (CV) related to face detection are the difficulty of acquisition in the target domain and the degradation of image quality. Especially in low-light situations, the poor visibility of images is difficult to label, which results in detectors trained under well-lit conditions exhibiting reduced performance in low-light environments. Conventional works image enhancement and object detection techniques are unable to resolve the inherent difficulties in collecting and labeling low-light images. The Dark-Illuminated Network with Contrastive Language–Image Pretraining (CLIP) and Self-Supervised Vision Transformer (Dino), abbreviated as DAl-CLIP-Dino is proposed to address the degradation of object detection performance in low-light environments and achieve zero-shot day–night domain adaptation. Specifically, an advanced reflectance representation learning module (which leverages Retinex decomposition to extract reflectance and illumination features from both low-light and well-lit images) and an interchange–redecomposition coherence process (which performs a second decomposition on reconstructed images after the exchange to generate a second round of reflectance and illumination predictions while validating their consistency using redecomposition consistency loss) are employed to achieve illumination invariance and enhance model performance. CLIP (VIT-based image encoder part) and Dino have been integrated for feature extraction, improving performance under extreme lighting conditions and enhancing its generalization capability. Our model achieves a mean average precision (mAP) of 29.6% for face detection on the DARK FACE dataset, outperforming other models in zero-shot domain adaptation for face detection.