| Low-level enhancement and high-level visual understanding in low-light vision have traditionally been treated separately. Low-light enhancement improves image quality for downstream tasks but has limited generalization. Low-light visual understanding, constrained by scarce labeled data, primarily relies on task-specific domain adaptation, which lacks scalability. To address these challenges, we build a generalized bridge between low-light enhancement and low-light understanding, which we term **Generalized Enhancement For Understanding (GEFU)**. This paradigm improves both **generalization** and **scalability**. To tackle the diverse causes of low-light degradation, we propose **Semantically Consistent Unsupervised Fine-tuning (SCUF)**. Extensive experiments demonstrate that our proposed method outperforms current state-of-the-art approaches in terms of traditional image quality as well as GEFU tasks, including classification, detection, and semantic segmentation. |
