The global knowledge asymmetries are increasingly interrogated by non-Western humanities and social sciences (HSS) scholars whose research is anchored in local contexts yet must adhere to international (Western) standards. Under this circumstance, the study aims to examine how the cultural self-awareness of non-Western HSS scholars is manifested in research through a Chinese lens. Based on previous theoretical perspectives and Fei Xiaotong’s theory of cultural self-awareness, the study first constructs two analytical dimensions: academic self-reflexivity and cultural appreciation attitudes. It then performs a qualitative investigation including 28 Mainland Chinese HSS scholars through interviews and literature analyses. The findings highlight key principles for academic self-reflexivity, namely reflecting on intellectual extraversion, dichotomous thinking, and the reemphasis on Chinese culture and knowledge. The cultural appreciation attitudes are also elaborated, which are embodied in the recognition and revaluation of traditional Chinese knowledge, the continued appreciation of modern Western knowledge, and the synthesis of different cultures and knowledge in research. These findings develop Fei’s cultural self-awareness theory and add new discourses to address global knowledge imbalances, promoting a more diverse and inclusive global higher education landscape.
Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. Despite impressive improvement, deep learning-based LLIE approaches require extensive training data, which is often difficult and costly to obtain. In this paper, we propose a zero-shot LLIE framework leveraging pre-trained latent diffusion models for the first time, which act as powerful priors to recover latent images from low-light inputs. Our approach introduces several components to alleviate the inherent challenges in utilizing pre-trained latent diffusion models, modeling the degradation process in an image-adaptive manner, penalizing the latent outside the manifold of natural images, and balancing the strengths of the guidance from the given low-light image during the denoising process. Experimental results demonstrate that our framework outperforms existing methods, achieving superior performance across various datasets.