This thesis focuses on Christine de Pizan’s mythological allegoric work entitled Epistre Othéa, written around 1400. True to the beliefs she portrays in many of her later seminal works, such as The Book of the City of Ladies, or The Treasure of the City of Ladies, Christine displays in this piece a strong didactic vision. The crucial pairing of text and image in the two manuscripts that I chose to focus on prove the power she exerted as a woman and as an artist but also mark her intention to strengthen her moral and political message through the use of different media. The purpose of this thesis will be to analyze the polyphonic voice that emerges from both the textual and pictorial elements of the Epistre in order to decipher Christine de Pizan’s distinctive ideology. I propose to examine the re-interpretation of two mythological figures, Medea and Minerva, in the Epistre and investigate the impact of this conscious manipulation of sources on Christine de Pizan’s overall works. Furthermore, the comparison between the figures of Othea and Christine herself is analyzed as a way to affirm the author’s idiosyncratic stance and delineate the scope of her proto-feminist views.

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This case study investigates if the AI text-to-image generator Midjourney can generate visual game character idea images for Dota 2 that meet the game’s art guidelines. The author defined the term “visual game character ideas” as the idea images at the early stage of visual character design process to help artists get inspiration. To achieve this, an experiment was designed and conducted where three new Dota 2 heroes’ backgrounds were developed by the author, and 32 images per hero were generated with Midjourney bot. These 96 images were evaluated to examine Midjourney’s performance based on seven aspects: accurate content, readability and identifiability, value gradient, value patterning, the number of colors, areas of rest and detail, and directionality. “YES” was given to each criterion if they meet the requirement. The value of this case study is to present the strength and weakness of the text-to-image generators for visual character design ideas, which can potentially show game artists when and how to use them in visual character design process. The result suggested that Midjourney could be used to generate visual character design ideas for Dota 2 unstably, and this instability was mainly caused by its identified flaw: content accuracy. Furthermore, it performed better for non-color-related aspects, while the performance for color-related items was significantly worse than others.

AI in art saw a huge spike in popularity with text-to-image models like Midjourney and Stable Diffusion. The AI aids in creation of 2D art and is at times able to save massive amounts of time. Creation of 3D assets is an incredibly time consuming task but the field is currently lacking in research pertaining to artificial intelligence. The goal of this study was to produce an AI-aided workflow that would be compared to a standard workflow of 3D art students. Participants were given one hour per workflow to produce game-ready sci-fi chair assets, one with their standard workflow and one with the study’s AI workflow where the AI tools supplemented or replaced parts of their regular workflow. They began with concepting and researching, moved onto modeling, sorted out the model’s UVs and finally textured the asset. Data taken from semi-structured interviews post-experiment was analyzed with thematic analysis to produce a vivid picture of their thoughts on the experience. The tools proved to be lackluster in both quality and user experience. It seems the tool that was most probable to see use in the future was a TTI tool for concepting. However, almost all tools – and the ideas behind the tools used showed great potential if developed further. The concept of AI in art was met with mixed emotions, excitement over the potential of improvements it might provide, and a small fear over the threat of being replaced. Considering how fast AI has developed in recent years, there is no doubt that further research on the topic is important. Even as the study was being conducted, new tools were being developed and released which could have found a way into the study or could prove useful for the next one.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.

Multimedia documents are composed of multiple file format combinations, such as image and text, image and sound, or image, text and sound. The type of multimedia document determines the form of analysis for knowledge architecture design and retrieval methods. Over the last few decades, theories of text analysis have been proposed and applied effectively. In recent years, theories of image and sound analysis have been proposed to work with text retrieval systems and progressed quickly due in part to rapid progress in computer processing speed. Retrieval of multimedia documents formerly was divided into the categories of image and text, and image and sound. While standard retrieval process begins from text only, methods are developing that allow the retrieval process to be accomplished simultaneously using text and image. Although image processing for feature extraction and text processing for term extractions are well understood, there are no prior methods that can combine these two features into a single data structure. This dissertation will introduce a common representation format for multimedia documents (CRFMD) composed of both images and text. For image and text analysis, two techniques are used: the Lorenz Information Measurement and the Word Code. A new process named Jeong's Transform is demonstrated for extraction of text and image features, combining the two previous measurements to form a single data structure. Finally, this single data measurements to form a single data structure. Finally, this single data structure is analyzed by using multi-dimensional scaling. This allows multimedia objects to be represented on a two-dimensional graph as vectors. The distance between vectors represents the magnitude of the difference between multimedia documents. This study shows that image classification on a given test set is dramatically improved when text features are encoded together with image features. This effect appears to hold true even when the available text is diffused and is not uniform with the image features. This retrieval system works by representing a multimedia document as a single data structure. CRFMD is applicable to other areas of multimedia document retrieval and processing, such as medical image retrieval, World Wide Web searching, and museum collection retrieval.

Esta dissertação se propõe, em um primeiro momento, a traçar um panorama histórico do surgimento e do desenvolvimento da literatura infantil no Ocidente, em especial nos centros que mais influenciaram o Brasil, como Inglaterra, França, Alemanha e Estados Unidos, abordando os autores, a produção e, quando preciso para a compreensão, o contexto histórico. Esse resgate se faz necessário para se chegar à criação do que atualmente chamamos de livro ilustrado contemporâneo, objeto em que as imagens são fundamentais para a construção da narrativa. A partir de então, este trabalho resgata as teorias que tentam compreender de que maneira as duas linguagens textual e imagética se inter-relacionam no livro ilustrado e apresenta uma análise de duas obras brasileiras: Inês, dos autores Roger Mello e Mariana Massarani, e Lá e Aqui, de Odilon Moraes e Carolina Moreyra. / This dissertation has the purpose of setting an historical cenario for the begining and the development of the infant literature in the ocident, mostly in England, France, Germany and The United States (the centers that has more influential power among brazilian authors). This piece approaches the authors, the production and, when it is necessary for fully undertanding, the historical context. This recaptulation is important to comprehend the path that leaded us to the modern picturebooks, in witch the images are a fundamental part of the narrative construction. After that, this piece recaptulates the theories that explain in whitch ways those two languages (text and image) correlate in the illustrated book. It also presents review of two remarkable brazilian books: Roger Mello and Mariana Massaranis book Inês and Odilon Moraes e Carolina Moreyras book Lá e Aqui (Here and There).

With a multitude of images available on the Internet, how do we find what we are looking for? This project tries to determine how much the precision and recall of search queries is improved by using a word taxonomy on traditional Text-Based Image Search and Content-Based Image Search. By applying a word taxonomy to different data sources, a strong keyword filter and a keyword extender were implemented and tested. The results show that depending on the implementation, the precision or the recall can be increased. By using a similar approach on real life implementations, it is possible to force images with higher precisions to the front while keeping a high recall value, thus increasing the experienced relevance of image search. / Med den mängd bilder som nu finns tillgänglig på Internet, hur kan vi fortfarande hitta det vi letar efter? Denna uppsats försöker avgöra hur mycket bildprecision och bildåterkallning kan öka med hjälp av appliceringen av en ordtaxonomi på traditionell Text-Based Image Search och Content-Based Image Search. Genom att applicera en ordtaxonomi på olika datakällor kan ett starkt ordfilter samt en modul som förlänger ordlistor skapas och testas. Resultaten pekar på att beroende på implementationen så kan antingen precisionen eller återkallningen förbättras. Genom att använda en liknande metod i ett verkligt scenario är det därför möjligt att flytta bilder med hög precision längre fram i resultatlistan och samtidigt behålla hög återkallning, och därmed öka den upplevda relevansen i bildsök.