Pre-trained Visual-Language Models (VLMs) have demonstrated powerful performance on various downstream tasks. Recently, many prompt tuning methods represented by Context Optimization (CoOp) have effectively adapted VLMs to few-shot tasks. However, the CoOp-based methods suffer from overfitting to base classes, which impairs the model’s generalization to new classes. Considering that meta-learning excels at generalizing to new classes, we combine meta-learning with CoOp-like vision-language model fine-tuning methods to improve performance on few-shot generation tasks. In this paper, we present a novel Meta-learning-based Multi-Textual Prompt tuning (MMTP) method, which learns multiple textual prompts leveraging meta-learning to enhance the visual-language model’s representation and generalization capabilities. Specifically, we introduce multi-textual prompts to enhance the representation of the model for improving the recognition of base classes. Simultaneously, we employ meta-learning to optimize prompt training, bolstering the model’s generalization to new classes. Extensive experiments demonstrate the superiority of our method under base-to-new generalization and cross-domain generalization settings. Furthermore, we also conduct ablation studies to validate the effectiveness of each component.

Low-light vision remains a fundamental challenge in computer vision due to severe illumination degradation, which significantly affects the performance of downstream tasks such as detection and segmentation. While recent state-of-the-art methods have improved performance through invariant feature learning modules, they still fall short due to incomplete modeling of low-light conditions. Therefore, we revisit low-light image formation and extend the classical Lambertian model to better characterize low-light conditions. By shifting our analysis to the frequency domain, we theoretically prove that the frequency-domain channel ratio can be leveraged to extract illumination-invariant features via a structured filtering process. We then propose a novel and end-to-end trainable module named \textbf{F}requency-domain \textbf{R}adial \textbf{B}asis \textbf{Net}work (\textbf{FRBNet}), which integrates the frequency-domain channel ratio operation with a learnable frequency domain filter for the overall illumination-invariant feature enhancement. As a plug-and-play module, FRBNet can be integrated into existing networks for low-light downstream tasks without modifying loss functions. Extensive experiments across various downstream tasks demonstrate that FRBNet achieves superior performance, including +2.2 mAP for dark object detection and +2.9 mIoU for nighttime segmentation. Code is available at: \url{https://github.com/Sing-Forevet/FRBNet}.