Large Scale Visual Recommendations From Street Fashion Images

We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. Finally, we also outline a large-scale annotated data set of fashion images (Fashion-136K) that can be exploited for future vision research

Are ugly products cool? A study on rebelliousness and product design preferences

Previous research has shown that consumers often prefer aesthetically pleasing over ugly products. However, a question arises whether this is always the case. This research examines whether consumers who are rebellious may perceive ugly products as cool and subsequently prefer these over beautiful ones. To test this prediction, an experiment was conducted ,and data was analyzed using Hayes Model 1, ANOVA and ANCOVA. The results showed that rebelliousness is not related to perception of coolness on ugly products, but originality is. Finally, possible future mechanisms are suggested, and managerial implications are discussed based on the findings

An Earth Science Scrapbook Project as an Alternative Assessment Tool

"Scrapbooking" is a popular hobby and as such, has found its way into educational settings, primarily in middle and elementary school levels. This article describes a scrapbook project that is used both as a means of demonstrating the connections between geology and students' daily lives and as an alternative form of assessment. The project was developed for an introductory Earth Science class for middle school and high school pre-service teachers. Educational levels: Graduate or professional

Using data visualization to deduce faces expressions

Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio

ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current \textbf{state-of-the-art} model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at \url{https://huggingface.co/Tyrannosaurus/ArtGPT-4}.Comment: 16 page

Exploring the role of design quality in the Building Schools for the Future programme

The Building Schools for the Future (BSF) programme represents the biggest single UK government investment in school buildings for more than 50 years. A key goal for BSF is to ensure that pupils learn in 21st-century facilities that are designed or redesigned to allow for educational transformation. This represents a major challenge to those involved in the design of schools. The paper explores the conceptualizations of design quality within the BSF programme. It draws on content analysis of influential reports on design published between 2000 and 2007 and interviews with key actors in the provision of schools. The means by which design quality has become defined and given importance within the programme through official documents is described and compared with the multiple understandings of design quality among key stakeholders. The findings portray the many challenges that practitioners face when operationalizing design quality in practice. The paper concludes with reflections on the inconsistencies between how design quality has been appropriated in the BSF programme and how it is interpreted and adopted in practice