In recent years, large-scale artificial intelligence (AI) models have become a focal point in technology, attracting widespread attention and acclaim. Notable examples include Google’s BERT and OpenAI’s GPT, which have scaled their parameter sizes to hundreds of billions or even tens of trillions. This growth has been accompanied by a significant increase in the amount of training data, significantly improving the capabilities and performance of these models. Unlike previous reviews, this paper provides a comprehensive discussion of the algorithmic principles of large-scale AI models and their industrial applications from multiple perspectives. We first outline the evolutionary history of these models, highlighting milestone algorithms while exploring their underlying principles and core technologies. We then evaluate the challenges and limitations of large-scale AI models, including computational resource requirements, model parameter inflation, data privacy concerns, and specific issues related to multi-modal AI models, such as reliance on text-image pairs, inconsistencies in understanding and generation capabilities, and the lack of true “multi-modality”. Various industrial applications of these models are also presented. Finally, we discuss future trends, predicting further expansion of model scale and the development of cross-modal fusion. This study provides valuable insights to inform and inspire future future research and practice.

Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknow low-light conditions. In this paper, we propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various low-light conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. The proposed method is evaluated on LOL and LOL-V2 datasets, the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-arts.