Search Results (24)

(1) Background: From a post-secular perspective, the relationship between religions in the public sphere is conceived as an exchange in which religious beliefs, when formulated as rational arguments, contribute to building a shared public culture and foster a democratic transformation of interreligious relations. This article critiques this approach, highlighting its neglect of the lived experience of religion and, in particular, the situated and situational nature of processes of religious identity and religious difference formation. (2) Methods: Ethnographic observation of a performance held in a sacred art museum in Tuscany by immigrants from different religious backgrounds, four semi-structured interviews with performers, and one interview with the museum director were conducted. (3) Results: Personal and religious narratives, along with face-to-face interactions, generate dynamics of identification, differentiation, and situated identity redefinition. Interaction with the artwork, framed as a shared space, facilitates shifts in religious self and other positioning. It also reconfigures the boundaries between “us” and “them.” The artwork acts as a symbolic device that enables multiple interpretations and unexpected forms of recognition. (4) Conclusions: Relations of identification and distinction among religious identities are transformed not through abstract rational deliberation but through concrete, discursive, and performative practices. Full article

Despite widespread recognition of Confucianism’s cultural importance among Malaysian Chinese communities, little is known about how its philosophical principles are reinterpreted and visually transformed by contemporary artists navigating postcolonial realities. This research addresses this gap through a mixed-method approach, combining quantitative data from 227 fine arts students, qualitative interviews with five representative Malaysian Chinese painters, and visual analysis of 50 key artworks. The results show that Confucianism functions not as a fixed doctrinal system but as a vital meta-framework that allows for the reimagining of core concepts, such as Ren (Benevolence) and He (Harmony), into tools for social critique and cultural negotiation. These ideas are expressed not through illustrative methods but via innovative symbolic and material strategies—ranging from fractured composition to technical experimentation—leading to a variety of personal styles rooted in a shared cultural logic. The study introduces a “critical translation” model for understanding the modernization of traditional philosophies within diaspora contexts, offering both theoretical insights and practical avenues for decolonizing arts education and fostering globally relevant, culturally authentic artistic practices. Full article

Embodied conversational agents (ECAs) are autonomous interaction interfaces designed to communicate with humans. This study investigates the impact of response delays and emotional facial expressions of ECAs on user perception and engagement. The motivation for this study stems from the growing integration of ECAs in various sectors, where their ability to mimic human-like interactions significantly enhances user experience. To this end, we developed an ECA with multimodal emotion recognition, both with voice and facial feature recognition and emotional facial expressions of the agent avatar. The system generates answers in real time based on media content. The development was supported by a case study of artwork images with the agent playing the role of a museum curator, where the user asks the agent for information on the artwork. We evaluated the developed system in two aspects. First, we investigated how the delay in an agent’s responses influences user satisfaction and perception. Secondly, we explored the role of emotion in an ECA’s face in shaping the user’s perception of responsiveness. The results showed that the longer response delay negatively impacted the user’s perception of responsiveness when the ECA did not express emotion, while the emotional expression improved the responsiveness perception. Full article

This study aims to explore the application of artificial intelligence (AI) technology in the preservation and exhibition of artworks, with the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System”, developed by Cheng Shiu University, as case studies. In recent years, AI technology has made significant advancements in image recognition, machine learning, and data analysis, which provide new opportunities for art management. However, due to high costs and implementation challenges, as well as a lack of qualified personnel to use these tools and systems, small art galleries and museums have not yet had the opportunity to acquire such systems. Therefore, this study observes the practical application of the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System” in the fields of art preservation and exhibition. The study employs case study and observation methods, with participatory observation as the primary data collection approach. The results indicate that AI technology significantly enhances the preservation conditions of artworks and the interactivity of exhibitions. The paper suggests that future efforts should focus on long-term planning relating to technology costs and professional talent development to fully realize the potential of AI in art management and exhibition. Additionally, the application of these technologies can be extended to other fields. Full article

This article offers a discussion of the possibility of distinguishing ochres from Mars pigments. The discussion addresses technological, archaeological, and artistic aspects. Natural earth pigments such as ochres, siennas, and umbers have been widely used from the Paleolithic to the present day and still find wide application despite the development of synthetic iron oxide pigment synthesis processes, called Mars pigments. The potential ability of today’s analytical techniques to distinguish between two classes of pigments of the same color with very similar chemical composition—but perhaps sufficient for reliable recognition—is also discussed. The paper begins by addressing the proper use of the terms “ochres” and “Mars pigments” and their accurate identification in artworks. It reviews the literature on the chemical–mineralogical characterization of yellow and red iron pigments and analyzes pigment catalogs to understand how companies distinguish ochres from Mars pigments. An experimental analysis using External Reflection Infrared Spectroscopy (FTIR-ER) compared painting samples made with natural ochres and Mars pigments, confirming the literature findings and suggesting future research directions. Key differences such as hematite in yellow ochres and specific spectral peaks in red ochres support the potential of FTIR-ER spectroscopy as a noninvasive tool for distinguishing pigments, especially for fragile artifacts and archaeological applications. Full article

Art evokes a wide range of effects, including emotional, cognitive, and behavioral outcomes. Despite the growing recognition of the role of art in tourism and hospitality, there is a gap in understanding how the different attributes of visual art influence guests’ responses. This perspective paper aims to address this gap by introducing a conceptual framework of the effects of visual art attributes on guests’ perceptions and behavioral intentions and calls for future research on the effects of different art characteristics in hospitality settings. Various attributes of visual art, such as the style, content, size, context, color palette, and thematic relevance of artworks, can serve as antecedents to guest experiences. Several factors can moderate the relationships between different art attributes and perceptions of art, including socio-demographic characteristics, personality traits, cultural differences, familiarity with art, and prior experiences. Hotel or vacation rental characteristics, including price, service quality, ratings, amenities, and other factors, can also interact with art perceptions in shaping attitudes toward the accommodation and behavioral intentions. A combination of survey methods, interviews, experience sampling, experiments, and emerging techniques can be applied in future studies to explore the effects of visual art and its attributes on guest and business outcomes. The proposed framework brings important implications for both academic scholarship and industry practice. Full article

This study presents a comprehensive literature review on the convergence of affective computing, interactive installation art, multi-dimensional sensory stimulation, and artificial intelligence (AI) in measuring emotional responses, demonstrating the potential of artificial intelligence in emotion recognition as a tool for sustainable development. It addresses the problem of understanding emotional response and measurement in the context of interactive installation art under artificial intelligence (AI), emphasizing sustainability as a key factor. The study aims to fill the existing research gaps by examining three key aspects: sensory stimulation, multi-dimensional interactions, and engagement, which have been identified as significant contributors to profound emotional responses in interactive installation art. The proposed approach involves conducting a process analysis of emotional responses to interactive installation art, aiming to develop a conceptual framework that explores the variables influencing emotional responses. This study formulates hypotheses that make specific predictions about the relationships between sensory stimulation, multi-dimensional interactions, engagement, and emotional responses. By employing the ASSURE model combined with experimental design, the research methodology ensures a systematic and comprehensive study implementation. The implications of this project lie in advancing the understanding of emotional experiences in interactive installation art under AI, providing insights into the underlying mechanisms that drive these experiences, and their influence on individual well-being from a sustainable perspective. The contributions of this research include bridging the identified research gaps, refining theoretical frameworks, and guiding the design of more impactful and emotionally resonant interactive artworks with sustainability in mind. This research seeks not only to fill the existing gaps in understanding emotional experiences in interactive installation art, but also to guide the development of immersive and emotionally engaging installations, ultimately advancing the broader field of human–computer interaction, promoting individual well-being, and contribute to sustainable development. Full article

Currently, a substantial portion of images snapped at exhibitions and galleries on social media demonstrates that aesthetic experience is not restricted to the confines of cultural institutions. The primary objective of this paper is to examine whether the content or aspect of an artwork influences the aesthetic experience of the viewer and to measure the artwork’s social media popularity. To compare controversial works of art with those whose design, qualities, or intended message are non-controversial, we first sought out controversial works. A variety of artworks were revealed on Instagram; thus, the objective was to identify a non-controversial artwork published in the same year as each controversial artwork. We adhered to the complete procedure for cleansing, standardizing, and transforming the data to ensure comparability. Popularity was measured using a ranking algorithm and quantitative approaches for the recognition and statistical measurement of emotions. In addition, the exhaustive literature survey on models of aesthetic experience revealed no link between the experience of art and its social media popularity. Considering this, we have proposed, among other things, a new framework for interacting with art that integrates these parameters. According to the findings, controversial artworks elicited stronger emotions than non-controversial artworks. Furthermore, investigations have determined the three most popular works of art in each category. Under the scrutiny of social media, these results may inspire future research on the popularity of museum artworks and the design of aesthetic experiences. Full article

Although real-image emotion recognition has been developed in several studies, an acceptable accuracy level has not been achieved in portrait drawings. This paper proposes a portrait emotion recognition framework based on independent component analysis (ICA) and latent codes to overcome the performance degradation problem in drawings. This framework employs latent code extracted through a generative adversarial network (GAN)-based encoder. It learns independently from factors that interfere with expression recognition, such as color, small occlusion, and various face angles. It is robust against environmental factors since it filters latent code by adding an emotion-relevant code extractor to extract only information related to facial expressions from the latent code. In addition, an image is generated by changing the latent code to the direction of the eigenvector for each emotion obtained through the ICA method. Since only the position of the latent code related to the facial expression is changed, there is little external change and the expression changes in the desired direction. This technique is helpful for qualitative and quantitative emotional recognition learning. The experimental results reveal that the proposed model performs better than the existing models, and the latent editing used in this process suggests a novel manipulation method through ICA. Moreover, the proposed framework can be applied for various portrait emotion applications from recognition to manipulation, such as automation of emotional subtitle production for the visually impaired, understanding the emotions of objects in famous classic artwork, and animation production assistance. Full article

We present a GAN-based model that rotates the faces in artistic portraits to various angles. We build a dataset of artistic portraits for training our GAN-based model by applying a 3D face model to the artistic portraits. We also devise proper loss functions to preserve the styles in the artistic portraits as well as to rotate the faces in the portraits to proper angles. These approaches enable us to construct a GAN-based face rotation model. We apply this model to various artistic portraits, including photorealistic oil paint portraits, watercolor portraits, well-known portrait artworks and banknote portraits, and produce convincing rotated faces in the artistic portraits. Finally, we prove that our model can produce improved results compared with the existing models by evaluating the similarity and the angles of the rotated faces through evaluation schemes including FID estimation, recognition ratio estimation, pose estimation and user study. Full article

The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors. Full article

Artificial intelligence research in natural language processing in the context of poetry struggles with the recognition of holistic content such as poetic symbolism, metaphor, and other fine-grained attributes. Given these challenges, multi-modal image–poetry reasoning and retrieval remain largely unexplored. Our recent accessibility study indicates that poetry is an effective medium to convey visual artwork attributes for improved artwork appreciation of people with visual impairments. We, therefore, introduce a deep learning approach for the automatic retrieval of poetry suitable to the input images. The recent state-of-the-art CLIP provides a way for multi-modal visual and text features matched using cosine similarity. However, it lacks shared cross-modality attention features to model fine-grained relationships. The proposed approach in this work takes advantage of strong pre-training of the CLIP model and overcomes its limitations by introducing shared attention parameters to better model the fine-grained relationship between both modalities. We test and compare our proposed approach using the expertly annotated MiltiM-Poem dataset, which is considered the largest public image–poetry pair dataset for English poetry. The proposed approach aims to solve the problems of image-based attribute recognition and automatic retrieval for fine-grained poetic verses. The test results reflect that the shared attention parameters alleviate fine-grained attribute recognition, and the proposed approach is a significant step towards automatic multi-modal retrieval for improved artwork appreciation of people with visual impairments. Full article

Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies. Full article

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the model to focus on learning informative and refined representations for both feature extraction and modality fusion. The resulting system can be used to categorize artworks according to the emotions they evoke; recommend paintings that accentuate or balance a particular mood; search for paintings of a particular style or genre that represents custom content in a custom state of impact. Experimental results on the WikiArt emotion dataset showed the efficiency of the approach proposed and the usefulness of three modalities in emotion recognition. Full article

This article focuses on the social/cultural representations of the statue of A Real Birmingham Family cast in bronze and unveiled in Britain’s second city in October 2014. It reveals a family comprising two local mixed-race sisters, both single mothers, and their sons, unanimously chosen from 372 families. Three of the four families shortlisted for the statue were ‘mixed-race’ families. The artwork came about through a partnership between the sculptress, Gillian Wearing, and the city’s Ikon Gallery. A number of different lay representations of the artwork have been identified, notably, that it is a ‘normal family with no fathers’ and that it is not a ‘typical family’. These are at variance with a representation based on an interpretation of the artwork and materials associated with its creation: that a nuclear family is one reality amongst many and that what constitutes a family should not be fixed. This representation destabilizes our notion of the family and redefines it as empirical, experiential, and first-hand, families being brought into recognition by those in the wider society who choose to nominate themselves as such. The work of Ian Hacking, Richard Jenkins, and others is drawn upon to contest the concept of ‘normality’. Further, statistical data are presented that show that there is now a plurality of family types with no one type dominating or meriting the title of ‘normal’. Finally, Wearing’s statues of families in Trentino and Copenhagen comprise an evolving body of cross-national public art that provides further context and meaning for this representation. Full article