In the era of GenAI, user attitude—shaped by cognition, emotion, and behavior—plays a critical role in the sustainable development of human–AI interaction. Human creativity and intelligence, as core drivers of social progress, are important factors influencing user attitudes. This paper systematically reviews 243 peer-reviewed studies on GenAI user attitudes published since 2019, identifying major research methods and theoretical perspectives, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the AI Device Use Acceptance (AIDUA) model. Drawing on contemporary creativity theories—such as Sternberg’s Theory of Successful Intelligence, the 4C Model by Kaufman and Beghetto, and the Dynamic Creativity Framework—we analyze how creativity and intelligence are conceptualized in current studies and how they affect user responses to GenAI. Through cross-cultural analysis and multimodal comparison, this review offers a comprehensive understanding of the interplay between GenAI and human creativity, aiming to support more inclusive and sustainable human–AI collaboration.

This study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such as Bibliometrix (version 4.1), VOSviewer (version 1.6.19), and CiteSpace (version 6.3.R1) on the relevant literature from the Web of Science Core Collection (WoSCC). The analysis reveals a significant increase in publications since 2017. Kerry J. Ressler has emerged as the most influential author in the field to date. The United States leads in the number of publications, producing seven times more papers than Canada, the second-ranked country, and demonstrating substantial influence. Harvard University and the Veterans Health Administration are also key institutions in this field. The Journal of Affective Disorders has the highest number of publications and impact in this area. In recent years, keywords related to functional connectivity, risk factors, and algorithm development have gained prominence. The field holds immense research potential, with AI poised to revolutionize PTSD management through early symptom detection, personalized treatment plans, and continuous patient monitoring. However, there are numerous challenges, and fully realizing AI’s potential will require overcoming hurdles in algorithm design, data integration, and societal ethics. To promote more extensive and in-depth future research, it is crucial to prioritize the development of standardized protocols for AI implementation, foster interdisciplinary collaboration—especially between AI and neuroscience—and address public concerns about AI’s role in healthcare to enhance its acceptance and effectiveness.

Thangka cultural elements detection aims to locate and identify instances in Thangka. However, as a unique form of pictorial art, Thangka exhibits distinct spatial structures that deviate significantly from general images in scale and density. Therefore, it is challenging for most state-of-the-art detectors designed for natural scenes to handle Thangka cultural elements detection effectively. To overcome this issue, we propose a multi-scale and dense object detector referred as MDDet. It embeds a multi-scale receptive field fusion module (MRF) that enlarges the receptive field while capturing the spatial and channel relationships at different scales, which significantly enriches the multi-scale features extracted from the backbone. In addition, we introduce a threshold-slicing aided hyper inference (T-SAHI) scheme, which adaptively slices images in dense scenarios to aid with dense object detection in the test time. We thoroughly evaluate our method, and MDDet outperforms the prior art by a clear margin on the Thangka dataset, achieving an absolute improvement of 1.9% in average precision (AP). For the challenging medium and small objects in Thangka, MDDet obtains wide margins of 12% and 3.7% in accuracy improvement, respectively. It also shows strong generalization ability when evaluated on general scenarios, e.g., Pascal VOC 2007 and MS COCO, validating the role of MDDet in object detection.

Bias and hallucinations in low-resource cultural artefacts significantly impede text-to-image generation models from understanding and disseminating. Focusing on Tibetan as a Chinese minority culture, we produced a children’s picture book through two methods: AI generation and human illustrator. Eye-tracking experiments were employed to investigate participants’ implicit attitudes, aesthetic biases, and cultural perceptions towards these two sources. The results revealed that (1) the hand-drawn group demonstrated higher fidelity to Tibetan culture, exhibiting a positive aesthetic calibration effect in terms of cultural adaptability owing to viewers’ attention duration to the cultural symbols details. (2) The AI-generated group elicited greater viewer interest and emotional engagement through its asymmetric color palettes, especially in color richness and stylistic rendering, and achieved professional-level compositional maturity in multi-character scene generation. This study provides empirical evidence to inform the division of labor between humans and AI in children’s book illustration and explores potential models for future human-AI collaboration.

Techniques for removing image reflections can effectively eliminate artifacts superimposed on the subject due to factors such as reflections in light or through glass. Most current methods are designed on the assumption that reflection areas maintain the original image content. However, the neglect some extreme cases where there is no original information left, such as photos taken in museums with generated light spots.In this paper, we propose a novel model capable of further removing light spots reflections. Specifically, it takes an image with reflection contamination as input, and then guided by the proposed reflection classifiers and structure restorer, ultimately outputs a predicted transmission layer image.Experimental results demonstrate that the proposed model is applicable to different categories of reflection images, outperforming state-of-the-art reflection removal techniques.In summary, the proposed model improves the effect of image reflection technology based on artificial intelligence(AI) in the case of spot reflection.