Search Results (173)

Various heritage objects can be subjected to various types of biodegradation and biodeterioration. Mold fungi can destroy many types of art—be it monumental art or easel paintings. Tempera paintings on wood are at risk of biodeterioration, since the wide variety of organic and inorganic materials in art objects often provide an optimal habitat for biological colonization, causing aesthetic and structural damage. In this regard, timely identification and characterization of their microbiological destructive potential are critical. The fungi Syncephalastrum sp. STG-160 and Cladosporium sphaerospermum STG-161, isolated from bio-lesion sites of the 16th century icon “Descent into Hell” from State Tretyakov Gallery, Moscow, were identified and characterized morphologically and molecularly in our work. Syncephalastrum sp. was found in an unusual habitat that has not been previously described for this species. To determine the biodegradability of the identified fungi, their cells were inoculated onto mock layers—egg yolk ochre, cobalt green tempera pigments, and watercolor black. The results show that some pigments were more degradable than others. The addition of cobalt green completely inhibited STG-161 growth and significantly deceleratedSTG-160 mycelium development, most likely due to the presence of heavy metal ions in the pigment. Ochre, a frequently used pigment in restoration practice, is the most degradable material for Syncephalastrum sp. STG-160. Combining culture-dependent methods with SEM and fluorescence microscopy allowed us to identify an invisible individual spore of Syncephalastrum sp. STG-160 and a single hypha of Cladosporium sphaerospermum STG-161 directly on the icon’s surface in clean-contaminated zones, potentially allowing their development in cases of adverse temperature and humidity conditions. Therefore, in order to ensure rapid and effective conservation, it is crucial to assess and quantify the presence of biological systems causing damage to the heritage object itself as well as its individual art components. Full article

This study uniquely integrates physiological and subjective data to predict comfort. To optimize lighting conditions in art galleries, this study investigates the interactive effects of painting tones (cool, medium, warm) and illuminance levels (50, 150, 300 lx) on visual comfort. Using decorative paintings as experimental stimuli, 30 participants were exposed to nine distinct lighting scenarios. Subjective questionnaires and eye-tracking data were collected to establish five predictive models. An additional cohort of 10 participants served as an external validation set. Results indicate that the interaction between tone and illuminance exerts a significant influence on comfort. The optimal combinations identified were cool tone + 50 lx, warm tone + 150 lx, and medium tone + 300 lx. Among the models, XGBoost demonstrated superior predictive performance (R² = 0.928 in the test set; R² = 0.884 in external validation). SHAP analysis revealed that the coefficient of variation in pupil diameter was the most critical predictor, followed by fixation count and related features. Both global and individual feature contributions to comfort were elucidated, offering a robust scientific foundation for the precise regulation of lighting environments in art galleries. Full article

Sketch re-identification (Re-ID) aims to retrieve pedestrian photographs in the gallery dataset by a query sketch image drawn by professionals, which is crucial for criminal investigations and missing person searches in the field of public security. The main challenge of this task lies in bridging the significant modality gap between sketches and photos while extracting discriminative modality-invariant features. However, information asymmetry between sketches and RGB photographs, particularly the differences in color information, severely interferes with cross-modal matching processes. To address this challenge, we propose a novel network architecture that integrates multi-channel augmentation with hierarchically consistent disentanglement learning. Specifically, a multi-channel augmentation module is developed to mitigate the interference of color bias in cross-modal matching. Furthermore, a modality-disentangled prototype(MDP) module is introduced to decompose pedestrian representations at the feature level into modality-invariant structural prototypes and modality-specific appearance prototypes. Additionally, a cross-layer decoupling consistency constraint is designed to ensure the semantic coherence of disentangled prototypes across different network layers and to improve the stability of the whole decoupling process. Extensive experimental results on two public datasets demonstrate the superiority of our proposed approach over state-of-the-art methods. Full article

The first monographic exhibition dedicated to Vittore Carpaccio (ca. 1460–1525) in the US, and the first outside of Italy, was hosted at the National Gallery of Art in Washington, DC, from 20 Nov 2022 to 23 February 2023 (from where it went to Venice). Building on the research of art historians and experts on Venice and the larger Mediterranean region in the early modern period, this paper examines Carpaccio’s depiction of various “Turks” in some of the large narrative painting cycles (teleri) commissioned by the devotional confraternities (scuole) in Renaissance Venice. While Carpaccio’s and the larger Venetian familiarity with Islam, including Turks, has been studied, this paper compares various female figures in the St. Stephen cycle with those in his St. George cycle, situating them in the larger historical context of the commissioning scuole (Scuola di Santo Stefano and Scuola di San Giorgio degli Schiavoni, respectively). While attempting to uncover the significance, if not the identities, of a few individuals who stand out from the crowd, this paper urges caution when attempting to discern social history from a painting, much as we take literary texts (particularly those written well before our own times) with a grain of salt. Full article

This article analyzes responses of museums and art institutions in Israel to the events of October 7th. It stresses the public role of museums in times of crisis, and the ways that diverse curatorial choices reflect upon their institutions’ pursuits. It focuses on the case study of curatorial empathy, enacted at the Tel Aviv University Art Gallery, noting its aptness at times of crisis and trauma. The article claims that in a society that experiences both internal and external conflicts, the approach of empathy offers flexibility and openness that allow the museum to respond to public need on the one hand, and poses challenging questions on the other. Such questions are explored through the method of artistic-scientific dialogue. As contentions multiply, overlap, and contrast, the expansion of circles of identification becomes a key strategy in addressing this crisis. This essay argues that empathy is a more thoughtful and productive curatorial approach, because it emphasizes connection rather than only identity. From this perspective, the crisis that started on October 7th is not only that of war, loss, and grief, but also that of a threat to humanness under extreme angst. Full article

This study designed and empirically evaluated a method to enhance information accessibility for museum and art gallery visitors using a small Large Language Model (sLLM) based on the Retrieval-Augmented Generation (RAG) framework. Over 199,000 exhibition descriptions were collected and refined, and a question-answering dataset consisting of 102,000 pairs reflecting user personas was constructed to develop DocentGemma, a domain-optimized language model. This model was fine-tuned through Low-Rank Adaptation (LoRA) based on Google’s Gemma2-9B and integrated with FAISS and OpenSearch-based document retrieval systems within the LangChain framework. Performance evaluation was conducted using a dedicated Q&A benchmark for the docent domain, comparing the model against five commercial and open-source LLMs (including GPT-3.5 Turbo, LLaMA3.3-70B, and Gemma2-9B). DocentGemma achieved an accuracy of 85.55% and a perplexity of 3.78, demonstrating competitive performance in language generation and response accuracy within the domain-specific context. To enhance retrieval relevance, a Spatio-Contextual Retriever (SC-Retriever) was introduced, which combines semantic similarity and spatial proximity based on the user’s query and location. An ablation study confirmed that integrating both modalities improved retrieval quality, with the SC-Retriever achieving a recall@1 of 53.45% and a Mean Reciprocal Rank (MRR) of 68.12, representing a 17.5 20% gain in search accuracy compared to baseline models such as GTE and SpatialNN. System performance was further validated through field deployment at three major exhibition venues in Seoul (the Seoul History Museum, the Hwan-ki Museum, and the Hanseong Baekje Museum). A user test involving 110 participants indicated high response credibility and an average satisfaction score of 4.24. To ensure accessibility, the system supports various output formats, including multilingual speech and subtitles. This work illustrates a practical application of integrating LLM-based conversational capabilities into traditional docent services and suggests potential for further development toward location-aware interactive systems and AI-driven cultural content services. Full article

From the 17th century, European leisure travellers sought novel experiences, places and landscapes; they explored them within the context of contemporary, but temporally changing, social norms. Amongst travellers’ earliest motivations were reportage, curiosity and recuperation in managed landscapes. From the late 18th century, images in art galleries and then guidebooks directed leisure travellers into ‘wild’ places. Supporting and part-driving these developments were travel and antiquarian publications. That normalisation of ‘wild places’ exploration coincided with natural history’s popularisation. From the early 19th century, geosites were recognised, scientifically described, and popularised through a range of publications; this marked the beginning of geotourism. This can be contextualised within the rise in resort-based coastal tourism. These various themes are explored in relation to ‘Coastal Southern England’, an important tourism region from the early-18th century. By the Great War’s (1914–1918) close, its tourism patterns and nature, recognisable in present-day offerings, were established. Its development as a geotourism region can be conceptualised through the ‘travellers’ gaze’ and ‘adapted comfort zone’ models. Early geotourism literature and artistic representations, along with their creators’ biographies, could underpin modern geo-interpretation, of which some exemplars are given. General conclusions are drawn and future research suggested. Full article

The acoustics of St Paul’s Cathedral, London, have been discussed in previous studies as a space of historical, cultural, societal, and architectural interest in the capital city of the United Kingdom. This paper presents the results from recent acoustic measurements carried out within the space, making use of state-of-the-art measurement techniques and equipment. The results from these measurements provide a new perspective on the acoustic properties of different and distinct spaces within the cathedral, including coupling effects between the main areas, and the whispering gallery effect that can be heard around the walkway at the base of the dome. The discussion includes the analysis of room acoustic parameters included in the international standards and speech intelligibility parameters, and an indirect comparison between the techniques used here and those used in previous studies of this space. Full article

Vehicle re-identification (Re-ID) is a critical computer vision task that aims to match the same vehicle across spatially distributed cameras, especially in the context of remote sensing imagery. While prior research has primarily focused on Re-ID using remote sensing images captured from similar, typically elevated viewpoints, these settings do not fully reflect complex aerial-ground collaborative remote sensing scenarios. In this work, we introduce a novel and challenging task: aerial-ground cross-view vehicle Re-ID, which involves retrieving vehicles in ground-view image galleries using query images captured from aerial (top-down) perspectives. This task is increasingly relevant due to the integration of drone-based surveillance and ground-level monitoring in multi-source remote sensing systems, yet it poses substantial challenges due to significant appearance variations between aerial and ground views. To support this task, we present AGID (Aerial-Ground Vehicle Re-Identification), the first benchmark dataset specifically designed for aerial-ground cross-view vehicle Re-ID. AGID comprises 20,785 remote sensing images of 834 vehicle identities, collected using drones and fixed ground cameras. We further propose a novel method, Enhanced Self-Correlation Feature Computation (ESFC), which enhances spatial relationships between semantically similar regions and incorporates shape information to improve feature discrimination. Extensive experiments on the AGID dataset and three widely used vehicle Re-ID benchmarks validate the effectiveness of our method, which achieves a Rank-1 accuracy of 69.0% on AGID, surpassing state-of-the-art approaches by 2.1%. Full article

The Buades Gallery (1973–2003) was not merely a commercial space in Madrid. In the history of art in Spain, it served as a professional and political node for Spanish conceptualism, an art form which, due to its idiosyncrasies, required its own channels of distribution. This article seeks to examine the trajectory of Mercedes Buades in alignment with this movement, re-evaluating her role from a feminist perspective and highlighting the importance of certain agents who have traditionally been invisibilised. To this end, a theoretical approach is adopted, following the sociology of art and the social history of art, paying particular attention to the contributions of Enrico Castelnuovo, Pierre Bourdieu and Núria Peist. These frameworks enable an analysis of the role of the gallerist as a structuring agent within the artistic field, capable of generating symbolic capital and establishing dynamics of production, circulation and consumption in the context of post-Franco Spain, a country that lacked a consolidated museum infrastructure at the time. Even so, Mercedes Buades established a model of gallery practice that, beyond its commercial dimension, contributed decisively to the symbolic configuration of contemporary art in Spain and formed part of a network of artistic visibility that promoted experimental art. Full article

The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, granularity, and heterogeneity of historical data. This study addresses these issues through a case study on the Museo de América’s “Place of Provenance” data, proposing a methodology for data cleaning and evaluating geocoding accuracy using Nominatim, ArcGIS, and GeoNames APIs. We assess these APIs by quantifying geocoding errors through a “balance sheet” method, identifying instances of over-representation, under-representation, or neutral results for geographical regions. The effectiveness of each API is analysed using confusion matrices and interactive cartograms, offering insights into misallocations. Our findings reveal varying accuracy among the APIs in processing heterogeneous historical spatial data. Nominatim achieved a 40.91% neutral result in correctly geocoding countries, underscoring challenges in spatial data representation. This research provides valuable methodological experiences and insights for researchers and GLAM institutions working with cultural heritage datasets. By enhancing spatial dispersion visualisation, this work contributes to understanding cultural circulations and historical patterns. This interdisciplinary work was developed as part of the ClioViz project, integrating Data Science, data Visualisation, and art history. Full article

Text-based person search (TPS), a critical technology for security and surveillance, aims to retrieve target individuals from image galleries using textual descriptions. The existing methods face two challenges: (1) ambiguous attribute–noun association (AANA), where syntactic ambiguities lead to incorrect associations between attributes and the intended nouns; and (2) textual noise and relevance imbalance (TNRI), where irrelevant or non-discriminative tokens (e.g., ‘wearing’) reduce the saliency of critical visual attributes in the textual description. To address these aspects, we propose the dependency-aware entity–attribute alignment network (DEAAN), a novel framework that explicitly tackles AANA through dependency-guided attention and TNRI via adaptive token filtering. The DEAAN introduces two modules: (1) dependency-assisted implicit reasoning (DAIR) to resolve AANA through syntactic parsing, and (2) relevance-adaptive token selection (RATS) to suppress TNRI by learning token saliency. Experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate state-of-the-art performance, with the DEAAN achieving a Rank-1 accuracy of 76.71% and an mAP of 69.07% on CUHK-PEDES, surpassing RDE by 0.77% in Rank-1 and 1.51% in mAP. Ablation studies reveal that DAIR and RATS individually improve Rank-1 by 2.54% and 3.42%, while their combination elevates the performance by 6.35%, validating their synergy. This work bridges structured linguistic analysis with adaptive feature selection, demonstrating practical robustness in surveillance-oriented TPS scenarios. Full article

The 1913 Armory Show has long been celebrated as the moment when America was introduced to modern art. This formalistic understanding of the event, though, would miss another equally important development which would only be observed through a historical materialist methodology that would see it as a response to a crisis of over-supply in the art market. It remains the single primary exhibition staged by the short-lived Association of American Painters and Sculptors (AAPS), a secession from the National Academy of Design. Though they would not succeed in creating a long-term alternative to their rival, their exhibition expanded upon innovations by the 1912 Sonderbund exhibition in Cologne. Through an archival examination of the Armory Show’s sourcing methods, a paradigm shift can be observed leading away from the nineteenth-century salon model by changing the system of artists submitting works to a jury, to one of marketing artworks provided by dealers. This new role for dealers would lead the way to the contemporary art fair model where galleries are the key exhibition decision-makers. Full article

Integrated photonic biosensors are revolutionizing lab-on-a-chip technologies by providing highly sensitive, miniaturized, and label-free detection solutions for a wide range of biological and chemical targets. This review explores the foundational principles behind their operation, including the use of resonant photonic structures such as microring and whispering gallery mode resonators, as well as interferometric and photonic crystal-based designs. Special focus is given to the design strategies that optimize light–matter interaction, enhance sensitivity, and enable multiplexed detection. We detail state-of-the-art fabrication approaches compatible with complementary metal-oxide-semiconductor processes, including the use of silicon, silicon nitride, and hybrid material platforms, which facilitate scalable production and seamless integration with microfluidic systems. Recent advancements are highlighted, including the implementation of optofluidic photonic crystal cavities, cascaded microring arrays with subwavelength gratings, and on-chip detector arrays capable of parallel biosensing. These innovations have achieved exceptional performance, with detection limits reaching the parts-per-billion level and real-time operation across various applications such as clinical diagnostics, environmental surveillance, and food quality assessment. Although challenges persist in handling complex biological samples and achieving consistent large-scale fabrication, the emergence of novel materials, advanced nanofabrication methods, and artificial intelligence-driven data analysis is accelerating the development of next-generation photonic biosensing platforms. These technologies are poised to deliver powerful, accessible, and cost-effective diagnostic tools for practical deployment across diverse settings. Full article

Face recognition (FR) is a less intrusive biometrics technology with various applications, such as security, surveillance, and access control systems. FR remains challenging, especially when there is only a single image per person as a gallery dataset and when dealing with variations like pose, illumination, and occlusion. Deep learning techniques have shown promising results in recent years using VAE and GAN, with approaches such as patch-VAE, VAE-GAN for 3D Indoor Scene Synthesis, and hybrid VAE-GAN models. However, in Single Sample Per Person Face Recognition (SSPP FR), the challenge of learning robust and discriminative features that preserve the subject’s identity persists. To address these issues, we propose a novel framework called AD-VAE, specifically for SSPP FR, using a combination of variational autoencoder (VAE) and Generative Adversarial Network (GAN) techniques. The proposed AD-VAE framework is designed to learn how to build representative identity-preserving prototypes from both controlled and wild datasets, effectively handling variations like pose, illumination, and occlusion. The method uses four networks: an encoder and decoder similar to VAE, a generator that receives the encoder output plus noise to generate an identity-preserving prototype, and a discriminator that operates as a multi-task network. AD-VAE outperforms all tested state-of-the-art face recognition techniques, demonstrating its robustness. The proposed framework achieves superior results on four controlled benchmark datasets—AR, E-YaleB, CAS-PEAL, and FERET—with recognition rates of 84.9%, 94.6%, 94.5%, and 96.0%, respectively, and achieves remarkable performance on the uncontrolled LFW dataset, with a recognition rate of 99.6%. The AD-VAE framework shows promising potential for future research and real-world applications. Full article