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1
Duez, Bernard. "Le transfert comme paradigme processuel de la groupalité psychique : de l'habitude au style". Revue de psychothérapie psychanalytique de groupe 45, n. 2 (2005): 31. http://dx.doi.org/10.3917/rppg.045.0031.
2
Okubo, Miki. "La notion "gothique" traduite dans la culture pop du Japon contemporain". Jangada: crítica | literatura | artes 1, n. 17 (6 agosto 2021): 390–408. http://dx.doi.org/10.35921/jangada.v1i17.351.
Abstract (sommario):
Le mouvement « Gothic and Lolita » est à la fois le style vestimentaire et une pratique philosophique développée dans la société japonaise depuis les années 1990, son influence se fait sentir dans différents milieux culturels et son origine est issue du roman gothique. Le présent article met en lumière un processus unique du transfert culturel de la notion « gothique » dans la culture japonaise contemporaine comme un phénomène culturel issu de différentes cultures, évolué au sein d’une culture autochtone plus traditionnelle. L’association de la notion « gothique » à la notion « lolita » est expliquée par deux causes : l’admiration totale des Japonais envers la culture occidentale et l’infantilisme comme trait culturel. Même si le concept de Lolita est emprunté au roman de Nabokov, sa signification est déformée dans le style Gothic and Lolita. Cette mode et sa pratique ont reçu une signification complexe de défense de soi avec des éléments gothiques afin de préserver à jamais la nature de Lolita et de refuser d’être consommée dans la société en tant que femme mûre. De cette façon, le concept gothique a été transformé et aliéné afin de s’adapter aux idées culturelles autochtones dans le transfert culturel vers la culture japonaise.
3
Koffi, Vivi, e Jean Lorrain. "L’intégration du successeur dans l’équipe de gestion des entreprises familiales : le cas des femmes chefs d’entreprise". Revue internationale P.M.E. 18, n. 3-4 (16 febbraio 2012): 73–92. http://dx.doi.org/10.7202/1008483ar.
Abstract (sommario):
La problématique de la relève dans les entreprises familiales préoccupe de plus en plus les chercheurs et les professionnels du milieu des affaires. Étant donné le nombre très important de transferts et la fréquence de leurs échecs, comment peut-on parler de relève sans s’intéresser à l’intégration du successeur dans l’équipe de gestion en vue d’assurer la pérennité de l’entreprise familiale ? De plus, compte tenu de l’importance grandissante de l’apport des femmes chefs d’entreprises dans l’économie des pays, l’objectif de cet article est de faire un survol des différents travaux qui portent, d’une part, sur le transfert managérial dans une optique d’intégration et, d’autre part, sur la particularité du style féminin des femmes chefs d’entreprises lors de l’intégration de leurs successeurs.
4
Gold, Iwala. "L’emprunt En Traduction". Tasambo Journal of Language, Literature, and Culture 3, n. 01 (15 febbraio 2024): 390–95. http://dx.doi.org/10.36349/tjllc.2024.v03i01.045.
Abstract (sommario):
Dans un monde où les connaissances évoluent d'une manière de plus en plus alerte, le transfert des mots d’une langue à une autre, en forme de l’emprunt, devient essentiel pour enrichir le vocabulaire des langues de travail. L’emprunt c’est l’acte de maintenir certains mots du texte de départ dans le texte d’arrivée, soit par manque d’équivalence soit style et goût du traducteur. Puisque le but de la traduction est le transfert d’une réalité idéologique, médical, religieux ou politique, d’une langue à une autre pour le bien de l’humanité, le transfert des lexiques devint aussi une évidence. Notre object est ici de montrer que l’emprunt est l’un des procédés de la traduction plutôt qu’un handicap lexical ou de vocabulaire. Cet article se base sur la théorie de la pertinence qui insiste sur le principe dans l’économie du langage selon lequel le traducteur ne traduit que ce qui est pertinent. Donc nous préconisons la procédure de ne traduire et de n’employer que ce qui est pertinent dans la pratique de la traduction.
5
Lonvaud-Funel, Aline. "La désacidification biologique des vins. Etat de la question, perspectives d'avenir". OENO One 28, n. 2 (30 giugno 1994): 161. http://dx.doi.org/10.20870/oeno-one.1994.28.2.1149.
Abstract (sommario):
<p style="text-align: justify;">La désacidification biologique est le résultat de la transformation enzymatique de l'acide L-malique. Levures et bactéries lactiques possèdent un équipement enzymatique, différent à la fois par la nature des produits de la transformation et leur affinité pour le substrat. La fermentation malolactique assurée par les bactéries lactiques est le procédé le plus répandu.</p><p style="text-align: justify;">Sa maîtrise passe par l'utilisation de levains bactériens, dont les plus récents sont ensemencés directement au vin après la fermentation alcoolique. Les résultats des recherches fondamentales sur l'enzyme malolactique permettent aujourd'hui d'envisager la construction de réacteurs enzymatiques et sans doute plus rapidement, le transfert de cette activité enzymatique chez les levures de vinification.</p>
6
Feuillat, François, Jean-René Perrin, René Keller, Danièle Aubert, Pierre Gelhaye, Claude Houssement, Jean Perrin e Pierre Michel. "Simulation expérimentale de « l'expérience tonneau ». Mesure des cinétiques d'imprégnation du liquide dans le bois et d'évaporation de surface". OENO One 28, n. 3 (30 settembre 1994): 227. http://dx.doi.org/10.20870/oeno-one.1994.28.3.1140.
Abstract (sommario):
<p style="text-align: justify;">Un modèle expérimental miniaturisé a été conçu dans le but de simuler "l'interface tonneau". Les cinétiques de l'imprégnation du liquide dans le bois et de l'évaporation de surface ont été mesurées en conditions de cave (15°C ; 90 p. cent HR) pendant 200 jours sur des petits disques à orientation tangentielle (Ø 63 mm; épaisseur 9 mm) à l'aide d'une méthodologie spécifique. Ces résultats expérimentaux ont été obtenus sur 4 espèces traditionnellement employées en tonnellerie (<em>Quercus</em> <em>robur </em>L., <em>Q. petraea</em> Liebl., <em>Castanea sativa</em> Mill., <em>Robinia pseudacacia</em> L.).</p><p style="text-align: justify;">L'analyse des cinétiques d'imprégnation et d'évaporation a permis de caractériser les régimes transitoire et permanent (état d'équilibre). Le régime transitoire est dominé par une forte imprégnation pendant les 40 premiers jours environ, qui décroît ensuite de façon asymptotique. Parallèlement, après un temps de latence appelé "retard à l'évaporation", le flux massique devient progressivement une fonction linéaire du temps. Le régime d'équilibre est atteint lorsque le niveau d'imprégnation de la pièce de bois est maximal et constant soit après 140 jours environ (profil d'humidité d'équilibre). Le fonctionnement en régime stationnaire se caractérise alors par un flux d'imprégnation à la face inteme équivalent au flux d'évaporation à la face exteme.</p><p style="text-align: justify;">Les variations quantitatives des phénomènes sont apparues liées aux conditions de transfert externe (HR de l'air ambiant) et aux conditions de transfert interne (propriétés de la pièce de bois). La comparaison d'échantillons toutes conditions égales par ailleurs a ainsi montré des différences significatives de comportements vraisemblablement liées à la variabilité anatomique du bois (interspécifique, interindividuelle et intraarbre).</p><p style="text-align: justify;">Cette variabilité pourrait avoir des conséquences sur les phénomènes oxydatifs et sur l'extraction des constituants du bois au cours de l'élevage des vins et des eaux-de-vie en fûts.</p>
7
Bouillon, Pierrette, e Katharina Boesefeldt. "Problèmes de traduction automatique dans les sous-langages des bulletins d’avalanches". Meta 37, n. 4 (30 settembre 2002): 635–46. http://dx.doi.org/10.7202/002108ar.
Abstract (sommario):
Résumé Dans cet article, nous décrivons l'état actuel du projet de traduction automatique des bulletins d'avalanches de la Suisse. Après un bref aperçu du projet, nous mettons en évidence les problèmes de traduction, auxquels nous avons été confronté lors du développement du projet et la manière de les résoudre avec ELU, environnement linguistique d'unification. Nous montrerons qu'une collaboration étroite lors de l'écriture des différentes grammaires nous permet généralement d'obtenir une même représentation sémantique dans les différentes langues, laquelle modélise le domaine des avalanches en Suisse. Mais nous maintiendrons aussi qu'un système de transfert est indispensable pour obtenir une traduction de qualité, qui ne provoque pas de modification du style des bulletins.
8
Bara, Florence, e Marie-France Morin. "Est-il nécessaire d’enseigner l’écriture script en première année ? Les effets du style d’écriture sur le lien lecture/écriture". Nouveaux cahiers de la recherche en éducation 12, n. 2 (30 luglio 2013): 149–60. http://dx.doi.org/10.7202/1017456ar.
Abstract (sommario):
Cette étude permet de questionner la tradition du double enseignement des styles d’écriture au Québec, en testant la validité d’un argument couramment avancé, qui suggère que la discordance entre l’écriture cursive et les caractères d’imprimerie nuirait au transfert entre la lecture et l’écriture. Les résultats obtenus auprès d’enfants de 2e année du primaire, ayant appris à écrire en script ou en cursive, ne nous permettent pas d’aller dans le sens de cet argument. En effet, les corrélations entre les performances en lecture et en écriture sont significatives et élevées pour les deux groupes d’enfants, et leurs performances sont similaires. Les apprenants semblent capables d’associer des caractères différents en lecture et en écriture, l’apprentissage de l’écriture cursive en 1re année ne nuirait pas à la lecture.
9
Dorin, Stéphane. "Style du Velours : sociologie du transfert de capital symbolique entre Andy Warhol et le Velvet Underground (1965-1967)". A contrario 3, n. 1 (2005): 45. http://dx.doi.org/10.3917/aco.031.67.
10
Handayani, Sri. "La chanson folklorique enfantine comme media de l’apprentissage interculturel et du transfert des valeurs morales". Digital Press Social Sciences and Humanities 3 (2019): 00040. http://dx.doi.org/10.29037/digitalpress.43313.
Abstract (sommario):
<p class="MsoNormal" style="text-indent:0in">Children's folk song is an example
of cultural products that has several goals in its creation. Aside from
entertaining, this type of song also aims to transfer moral values or character
building to children. Thanks to its moral values, this type of song is a
potential medium as a media of character learning and also intercultural
learning. This article aims to propose a compilation of Javanese children's
folk songs translated into French intended not only for Indonesian learners who
learn French, but also for French speakers who learn Indonesian language. The
research focused on the process of translating the Javanese folk songs into
French. This process is an assimilation of two different cultures by
accentuating on the promotion of the culture of origin through the language.
The future big goal is to make traditional songs better known around the world.
After applying the developmental research approach, seven Javanese children's
folk songs have been translated into French: <i>Gundhul pacul, Padhang mbulan, Menthok-menthok, Dhondhong opo salak,
Sluku-sluku bathok, Lir ilir</i>, and <i>Siji
lor otelu</i>. Each song has its value and also contains cultural elements. A
booklet and a CD containing the translation of Javanese children's folk songs
into French is the final product of this research that could be used in
intercultural learning and at the same time character building</p><p>
</p>
11
Jin, Xin. "Art Style Transfer of Oil Painting Based on Parallel Convolutional Neural Network". Security and Communication Networks 2022 (30 luglio 2022): 1–7. http://dx.doi.org/10.1155/2022/5087129.
Abstract (sommario):
To generate a new ornamental image, add an image’s oil painting style information to any image while preserving the image’s semantic content. With the rapid advancement of deep learning (DL), image style transfer has become one of the most active areas of computer vision research (CV). This paper proposes an oil painting style transfer technique based on parallel convolutional neural networks to address the ineffective style transfer of locally similar regions in content images and the slow processing speed of existing methods. By incorporating Gaussian sampling and a parallelization algorithm, this method effectively transfers the style of an oil painting. The algorithm can combine the content of any image with a variety of well-known oil painting styles to create high-quality works of art. The experimental results indicate that, compared to existing methods, the proposed method can effectively reduce the style loss of the generated image, make the generated image’s overall style more uniform, and produce a more pleasing visual effect.
12
Song, Young Do, e Eui Chul Lee. "Facial De-identification Using Neural Style Transfer". Journal of Next-generation Convergence Technology Association 6, n. 12 (31 dicembre 2022): 2240–48. http://dx.doi.org/10.33097/jncta.2022.06.12.2240.
13
Bigras, Élisabeth. "Petite fille? Femme?" Santé mentale au Québec 7, n. 1 (13 giugno 2006): 37–41. http://dx.doi.org/10.7202/030122ar.
Abstract (sommario):
Résumé Ce moment d'une rencontre psychanalytique avec une petite fille de 9 ans vise surtout à rendre compte de l'ambiance particulière qui se dégage d'une telle rencontre. Par ambiance, il faudrait entendre une qualité de vécu propre à la réalité psychique de l'enfant et à la façon dont elle s'exprime au niveau fantasmatique et onirique. Par ambiance, il faut également entendre la singularité et le style propre dans lequel s'exprime la vie psychique de cette petite fille lors de sa rencontre avec une analyste femme. On met aussi en évidence l'utilisation du contre-transfert de l'analyste qui lui permet de «coller au plus près» du déroulement de l'analyse et de respecter son originalité. Dans ce récit, on voit comment le rapprochement incestueux avec le père, «symptôme» pour lequel elle avait consulté, masquait en fait un désir de rapprochement et d'identification féminine à la mère, désir qui avait été refoulé du fait de l'absence effective de la mère. La récupération de ce désir a permis au jeu des identifications de reprendre son cours et a permis à la petite fille d'abandonner son comportement régressif à l'égard de son père tout en lui conservant son affection.
14
Horvitz, Zachary, Ajay Patel, Chris Callison-Burch, Zhou Yu e Kathleen McKeown. "ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer". Proceedings of the AAAI Conference on Artificial Intelligence 38, n. 16 (24 marzo 2024): 18216–24. http://dx.doi.org/10.1609/aaai.v38i16.29780.
Abstract (sommario):
Textual style transfer is the task of transforming stylistic properties of text while preserving meaning. Target "styles" can be defined in numerous ways, ranging from single attributes (e.g. formality) to authorship (e.g. Shakespeare). Previous unsupervised style-transfer approaches generally rely on significant amounts of labeled data for only a fixed set of styles or require large language models. In contrast, we introduce a novel diffusion-based framework for general-purpose style transfer that can be flexibly adapted to arbitrary target styles at inference time. Our parameter-efficient approach, ParaGuide, leverages paraphrase-conditioned diffusion models alongside gradient-based guidance from both off-the-shelf classifiers and strong existing style embedders to transform the style of text while preserving semantic information. We validate the method on the Enron Email Corpus, with both human and automatic evaluations, and find that it outperforms strong baselines on formality, sentiment, and even authorship style transfer.
15
Roman, Nicoleta. "Émigrants et réviseurs scolaires dans la Valachie de la première moitié du XIXe-siècle: transfert intellectuel et construction nationale. Étude de cas: Ioan Maiorescu". PLURAL. History, Culture, Society 11, n. 1 (30 settembre 2023): 5–36. http://dx.doi.org/10.37710/plural.v11i1_1.
Abstract (sommario):
The 19th century redefined the place of nations within empires. Enlightenment ideas of mother-tongue education, natural rights and liberty join the imperial center’s aim to attract loyalties, gain allegiance, create economic wealth and educate a citizenry attached to the state. In south-eastern Europe, the fragile coexistence of three empires (Ottoman, Russian and Austrian) was disrupted by the way in which the neighboring ethnic groups intended to adjust the Enlightenment ideas to suit the development of their own cultures. The present study traces the transfers brought by the Transylvanian intellectual émigrés to the field of education from Austrian Transylvania to the neighboring Ottoman principality of Wallachia. Coming from the ranks of the Greek Catholic intellectuals – an intermediary stratum between the imperial center, the privileged aristocracy and the majority of Romanian peasants – they pursued, at the same time, their professional development and the rise of literacy in Wallachia. As school inspectors, they intervened administratively and pedagogically in the school network recently established in Wallachia. Their leader, Ioan Maiorescu, questioned the Russian-style model of modernization and called for adapting Western models to the local reality to further the aim of cultural emancipation
16
Shen Yu, 沈瑜, 杨倩 Yang Qian, 苑玉彬 Yuan Yubin e 王霖 Wang Lin. "目标边缘清晰化的图像风格迁移". Laser & Optoelectronics Progress 58, n. 12 (2021): 1210021. http://dx.doi.org/10.3788/lop202158.1210021.
17
Wu, Guang Lin, Liang Sheng Zhu e Fang Cheng Li. "Numerical Simulation of Dissolved Oxygen Transfer in an Aerated Pond". Applied Mechanics and Materials 587-589 (luglio 2014): 588–93. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.588.
Abstract (sommario):
Dissolved oxygen transfer in an aeration pond was studied by numerical method in this paper. 2d water pond current induced by water-wheel aerators and oxygen transfer process was simulated by finite volume method. In order to verify the relationships between current and dissolved oxygen transfer, we set 4 layout styles with 4 aerators in a water pond. It is proved that different layout styles induce different flow structures and dissolved oxygen distribution. Traditional layout (style a) is not the best way for oxygen transfer, and other styles (style b, c &d) are all superior to the traditional layout. But water flow structures are not the same results. Water circulation performance is style a, c, b, d in sequence. Style c (flow to corners along pond wall) is the best way by comprehensive consideration of both water circulation performances and dissolved oxygen distribution.
18
Wang, Zhizhong, Lei Zhao, Zhiwen Zuo, Ailin Li, Haibo Chen, Wei Xing e Dongming Lu. "MicroAST: Towards Super-fast Ultra-Resolution Arbitrary Style Transfer". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 3 (26 giugno 2023): 2742–50. http://dx.doi.org/10.1609/aaai.v37i3.25374.
Abstract (sommario):
Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions.
19
Lin, Minxuan, Fan Tang, Weiming Dong, Xiao Li, Changsheng Xu e Chongyang Ma. "Distribution Aligned Multimodal and Multi-domain Image Stylization". ACM Transactions on Multimedia Computing, Communications, and Applications 17, n. 3 (22 luglio 2021): 1–17. http://dx.doi.org/10.1145/3450525.
Abstract (sommario):
Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform multimodal and multi-domain stylization simultaneously. In this study, we propose a unified framework for multimodal and multi-domain style transfer with the support of both exemplar-based reference and randomly sampled guidance. The key component of our method is a novel style distribution alignment module that eliminates the explicit distribution gaps between various style domains and reduces the risk of mode collapse. The multimodal diversity is ensured by either guidance from multiple images or random style codes, while the multi-domain controllability is directly achieved by using a domain label. We validate our proposed framework on painting style transfer with various artistic styles and genres. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate that our method can generate high-quality results of multi-domain styles and multimodal instances from reference style guidance or a random sampled style.
20
Divine, Deborah Reaves. "Adult Learning Styles and Effective Transfer of Information". Transportation Research Record: Journal of the Transportation Research Board 1637, n. 1 (gennaio 1998): 9–12. http://dx.doi.org/10.3141/1637-02.
Abstract (sommario):
Effective training and other technology transfer (T2) activities occur when teachers/trainers or T2 agents recognize and use training techniques that correspond to the learning styles of their learner audiences. Learning style is defined as one’s personally preferred way of processing information and experience. It crosses all content areas. It is affected by both intrinsic and extrinsic factors such as stress, world view, and environment. Each learner has the ability to utilize a variety of learning styles, and each teacher has a preferred teaching style based on his or her preferred information processing mode. Each learner learns most comfortably and fastest when his or her preferred learning style meshes with the teaching style of the teacher/trainer. Often teacher/trainers do not have the time, funds, or opportunity to determine their learners’ preferred styles. The effective teacher/trainer utilizes a variety of teaching techniques to engage and convey the most information to the most learners.
21
Ashtari, Amirsaman, Chang Wook Seo, Cholmin Kang, Sihun Cha e Junyong Noh. "Reference Based Sketch Extraction via Attention Mechanism". ACM Transactions on Graphics 41, n. 6 (30 novembre 2022): 1–16. http://dx.doi.org/10.1145/3550454.3555504.
Abstract (sommario):
We propose a model that extracts a sketch from a colorized image in such a way that the extracted sketch has a line style similar to a given reference sketch while preserving the visual content identically to the colorized image. Authentic sketches drawn by artists have various sketch styles to add visual interest and contribute feeling to the sketch. However, existing sketch-extraction methods generate sketches with only one style. Moreover, existing style transfer models fail to transfer sketch styles because they are mostly designed to transfer textures of a source style image instead of transferring the sparse line styles from a reference sketch. Lacking the necessary volumes of data for standard training of translation systems, at the core of our GAN-based solution is a self-reference sketch style generator that produces various reference sketches with a similar style but different spatial layouts. We use independent attention modules to detect the edges of a colorized image and reference sketch as well as the visual correspondences between them. We apply several loss terms to imitate the style and enforce sparsity in the extracted sketches. Our sketch-extraction method results in a close imitation of a reference sketch style drawn by an artist and outperforms all baseline methods. Using our method, we produce a synthetic dataset representing various sketch styles and improve the performance of auto-colorization models, in high demand in comics. The validity of our approach is confirmed via qualitative and quantitative evaluations.
22
Park, Jisun, e Kyungeun Cho. "Neural Rendering-Based 3D Scene Style Transfer Method via Semantic Understanding Using a Single Style Image". Mathematics 11, n. 14 (24 luglio 2023): 3243. http://dx.doi.org/10.3390/math11143243.
Abstract (sommario):
In the rapidly emerging era of untact (“contact-free”) technologies, the requirement for three-dimensional (3D) virtual environments utilized in virtual reality (VR)/augmented reality (AR) and the metaverse has seen significant growth, owing to their extensive application across various domains. Current research focuses on the automatic transfer of the style of rendering images within a 3D virtual environment using artificial intelligence, which aims to minimize human intervention. However, the prevalent studies on rendering-based 3D environment-style transfers have certain inherent limitations. First, the training of a style transfer network dedicated to 3D virtual environments demands considerable style image data. These data must align with viewpoints that closely resemble those of the virtual environment. Second, there was noticeable inconsistency within the 3D structures. Predominant studies often neglect 3D scene geometry information instead of relying solely on 2D input image features. Finally, style adaptation fails to accommodate the unique characteristics inherent in each object. To address these issues, we propose a novel approach: a neural rendering-based 3D scene-style conversion technique. This methodology employs semantic nearest-neighbor feature matching, thereby facilitating the transfer of style within a 3D scene while considering the distinctive characteristics of each object, even when employing a single style image. The neural radiance field enables the network to comprehend the geometric information of a 3D scene in relation to its viewpoint. Subsequently, it transfers style features by employing the unique features of a single style image via semantic nearest-neighbor feature matching. In an empirical context, our proposed semantic 3D scene style transfer method was applied to 3D scene style transfers for both interior and exterior environments. This application utilizes the replica, 3DFront, and Tanks and Temples datasets for testing. The results illustrate that the proposed methodology surpasses existing style transfer techniques in terms of maintaining 3D viewpoint consistency, style uniformity, and semantic coherence.
23
Chang, Chin-Chen, e Ping-Hao Peng. "Sand Painting Generation Based on Convolutional Neural Networks". Journal of Imaging 10, n. 2 (7 febbraio 2024): 44. http://dx.doi.org/10.3390/jimaging10020044.
Abstract (sommario):
Neural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content. In this paper, we propose a style transfer approach for sand painting generation based on convolutional neural networks. The proposed approach aims to improve sand painting generation via neural style transfer, which can address the problem of blurred objects. Furthermore, it can reduce background noise caused by neural style transfers. First, we segment the main objects from the content image. Subsequently, we perform close–open filtering operations on the content image to obtain smooth images. Subsequently, we perform Sobel edge detection to process the images and obtain edge maps. Based on these edge maps and the input style image, we perform neural style transfer to generate sand painting images. Finally, we integrate the generated images to obtain the final stylized sand painting image. The results show that the proposed approach yields good visual effects from sand paintings. Moreover, the proposed approach achieves better visual effects for sand painting than the previous method.
24
Farouk Soliman, Amal. "Investigating the Leadership Style Effect on the Technology Transfer Effectiveness: A Proposed New Model (An Applied Study on the Dairy Manufacturing Sector in Egypt)". International Journal of Business and Management 11, n. 10 (18 settembre 2016): 141. http://dx.doi.org/10.5539/ijbm.v11n10p141.
Abstract (sommario):
<p><strong>Purpose: </strong>Different leadership styles' effects on technology transfer effectiveness have not been implied in the literature. This study seeks to theoretically investigate the effect of five leadership styles; Classified according to personal authority of the leader; On technology transfer effectiveness.</p><p><strong>Design/methodology/approach:</strong> This study identifies five leadership styles which affect technology transfer effectiveness including transformational leadership, transactional leadership, visionary leadership, charismatic leadership and culture based leadership. This study associates these five leadership styles to technology transfer effectiveness. A conceptual model is tested using a survey data collected from a sample of manufacturing plants in the dairy manufacturing sector in Egypt.</p><p><strong>Findings: </strong>The results indicate that only four leadership styles significantly affect technology transfer effectiveness. Visionary leadership style has the strongest significant effect on technology transfer effectiveness, followed by culture based leadership style, then charismatic leadership style then transactional leadership. Transformational leadership does not significantly affect technology transfer effectiveness.</p><p><strong>Research Limitation/Implications: </strong>Transformational leadership has been observed to have no significant effect on technology transfer effectiveness. A set of refined transformational leadership measures should be developed in future studies.</p><p><strong>Practical Implications:</strong> This paper identifies the importance of leadership styles in achieving the success of technology transfer.</p><p><strong>Originality/value: </strong>This study provides a theoretical foundation for the effect of the leadership styles on technology transfer effectiveness in the dairy manufacturing plants. This study is one of the first efforts that empirically examine the effect of leadership styles on technology transfer effectiveness.</p><p> </p>
25
Yang, Shuai, Jiaying Liu, Wenjing Wang e Zongming Guo. "TET-GAN: Text Effects Transfer via Stylization and Destylization". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 1238–45. http://dx.doi.org/10.1609/aaai.v33i01.33011238.
Abstract (sommario):
Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via oneshot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.
26
Choi, Ha-Yeong, Sang-Hoon Lee e Seong-Whan Lee. "DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion". Proceedings of the AAAI Conference on Artificial Intelligence 38, n. 16 (24 marzo 2024): 17862–70. http://dx.doi.org/10.1609/aaai.v38i16.29740.
Abstract (sommario):
Diffusion-based generative models have recently exhibited powerful generative performance. However, as many attributes exist in the data distribution and owing to several limitations of sharing the model parameters across all levels of the generation process, it remains challenging to control specific styles for each attribute. To address the above problem, we introduce decoupled denoising diffusion models (DDDMs) with disentangled representations, which can enable effective style transfers for each attribute in generative models. In particular, we apply DDDMs for voice conversion (VC) tasks, tackling the intricate challenge of disentangling and individually transferring each speech attributes such as linguistic information, intonation, and timbre. First, we use a self-supervised representation to disentangle the speech representation. Subsequently, the DDDMs are applied to resynthesize the speech from the disentangled representations for style transfer with respect to each attribute. Moreover, we also propose the prior mixup for robust voice style transfer, which uses the converted representation of the mixed style as a prior distribution for the diffusion models. The experimental results reveal that our method outperforms publicly available VC models. Furthermore, we show that our method provides robust generative performance even when using a smaller model size. Audio samples are available at https://hayeong0.github.io/DDDM-VC-demo/.
27
Zhou, Chuying. "An emotional expression framework based on social media style transfer". Applied and Computational Engineering 20, n. 1 (23 ottobre 2023): 100–107. http://dx.doi.org/10.54254/2755-2721/20/20231080.
Abstract (sommario):
Recently, artificial intelligence has great effort in social media. Style transfer is a subtask of deep learning, can transform images shared on social media into works with artistic painting style transfer through image recognition technology, which creating amazing effects. However, the current style transfer technology is still in the early stages of development, and can only convert the shared picture into the style of a certain artwork, but does not have the ability to convert the emotional expression of the picture. In this paper, we proposes an emotional expression framework based on social media style transfer, conducted an experiment in the LFW database where the same portrait photo can express different emotions by combining it with images of different styles and realizes the style transfer technology for emotion and emotional expression. Therefore, the application of style transfer technology in social media is of great value and can promote the intelligent development of social media.
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Papti, Mr Madhu Kumar. "Multimodal Content Analysis Using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 12, n. 5 (31 maggio 2024): 564–69. http://dx.doi.org/10.22214/ijraset.2024.61566.
Abstract (sommario):
Abstract: The multimodal content analysis platform combines sentiment analysis and neural style transfertechniques to process and improve various types of digital content. The sentiment analysis module utilizes natural language processing (NLP) algorithms, such as recurrent neural networks (RNNs) or transformer models like BERT, to extract emotional signals from textual, visual, and auditory inputs. Signals are classified into predefined sentiment categories, providing granular insights into the emotional context of the content. The platform employs neural style transfer algorithms, such as style transfer networks (NSTNs) or generative adversarial networks (GANs), to transfer stylistic attributes between texts. By training on a diverse range of artistic styles, the system learns to apply these styles to input text while preserving semantic meaning. This process enhances the visual representation of textual content, making it more appealing and engaging to users.
29
Hu, Huiting, Wenxin Lian, Rui Su, Chongyu Ren e Juan Zhang. "Stylization of a Seismic Image Profile Based on a Convolutional Neural Network". Energies 15, n. 16 (20 agosto 2022): 6039. http://dx.doi.org/10.3390/en15166039.
Abstract (sommario):
Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences between optical image and seismic data, making it difficult for a model to learn seismic data characteristics. Therefore, a style transfer network is necessary to make the styles of optical image and seismic data more similar. Since the stylization effect of a seismic section is similar to that of most art styles, based on an in-depth study of image style transfer, this paper compared the effects of various style transfer models, and selected a Laplacian pyramid network to carry out a study of seismic section stylization. It transmits low-resolution global style patterns through a drafting network, revises high-resolution local details through correction networks, and aggregates all pyramid layers to output final stylized images of seismic profiles. Experiments show that this method can effectively convey the whole style pattern without losing the original image content. This style transfer method, based on the Laplacian pyramid network, provides theoretical guidance for the fast and objective application of the model to seismic data features.
30
Choi, Hyun-Chul. "Toward Exploiting Second-Order Feature Statistics for Arbitrary Image Style Transfer". Sensors 22, n. 7 (29 marzo 2022): 2611. http://dx.doi.org/10.3390/s22072611.
Abstract (sommario):
Generating images of artistic style from input images, also known as image style transfer, has been improved in the quality of output style and the speed of image generation since deep neural networks have been applied in the field of computer vision research. However, the previous approaches used feature alignment techniques that were too simple in their transform layer to cover the characteristics of style features of images. In addition, they used an inconsistent combination of transform layers and loss functions in the training phase to embed arbitrary styles in a decoder network. To overcome these shortcomings, the second-order statistics of the encoded features are exploited to build an optimal arbitrary image style transfer technique. First, a new correlation-aware loss and a correlation-aware feature alignment technique are proposed. Using this consistent combination of loss and feature alignment methods strongly matches the second-order statistics of content features to those of the target-style features and, accordingly, the style capacity of the decoder network is increased. Secondly, a new component-wise style controlling method is proposed. This method can generate various styles from one or several style images by using style-specific components from second-order feature statistics. We experimentally prove that the proposed method achieves improvements in both the style capacity of the decoder network and the style variety without losing the ability of real-time processing (less than 200 ms) on Graphics Processing Unit (GPU) devices.
31
Qian, Wenhua, Jinde Cao, Dan Xu, Rencan Nie, Zheng Guan e Rui Zheng. "CNN-Based Embroidery Style Rendering". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 14 (23 maggio 2020): 2059045. http://dx.doi.org/10.1142/s0218001420590454.
Abstract (sommario):
Nonphotorealistic rendering (NPR) techniques are used to transform real-world images into high-quality aesthetic styles automatically. NPR mainly focuses on transfer hand-painted styles to other content images, and simulates pencil drawing, watercolor painting, sketch painting, Chinese monochromes, calligraphy and, so on. However, digital simulation of Chinese embroidery style has not attracted researcher’s much attention. This study proposes an embroidery style transfer method from a 2D image on the basis of a convolutional neural network (CNN) and evaluates the relevant rendering features. The primary novelty of the rendering technique is that the strokes and needle textures are produced by the CNN and the results can display embroidery styles. The proposed method can not only embody delicate strokes and needle textures but also realize stereoscopic effects to achieve real embroidery features. First, using conditional random fields (CRF), the algorithm segments the target content and the embroidery style images through a semantic segmentation network. Then, the binary mask image is generated to guide the embroidery style transfer for different regions. Next, CNN is used to extract the strokes and texture features from the real embroidery images, and transfer these features to the content images. Finally, the simulating image is generated to show the features of the real embroidery styles. To demonstrate the performance of the proposed method, the simulations are compared with real embroidery artwork and other methods. In addition, the quality evaluation method is used to evaluate the quality of the results. In all the cases, the proposed method is found to achieve needle visual quality of the embroidery styles, thereby laying a foundation for the research and preservation of embroidery works.
32
Miah, Jonayet, Duc Minh Cao, Md Abu Sayed e Md Sabbirul Haque. "Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks". Journal of Computer Science and Technology Studies 5, n. 4 (17 novembre 2023): 78–85. http://dx.doi.org/10.32996/jcsts.2023.5.4.9.
Abstract (sommario):
Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content.
33
Liu, Xueyuan. "Research on Neural Style Transfer". Journal of Physics: Conference Series 2079, n. 1 (1 novembre 2021): 012029. http://dx.doi.org/10.1088/1742-6596/2079/1/012029.
Abstract (sommario):
Abstract The process of using CNN (Convolutional Neural Network) to blend the contents of a picture with different styles is called neural style transfer (NST). The purpose of this paper is to introduce current progress of NST, and introduce in detail the classification of the main NST algorithms based on deep learning, and make qualitative and quantitative comparisons of different algorithms, and then analyze the application prospects of image style migration in related fields, and finally summarize the existing problems and future research directions of NST.
34
Zhang, Yu, Rongjie Huang, Ruiqi Li, JinZheng He, Yan Xia, Feiyang Chen, Xinyu Duan, Baoxing Huai e Zhou Zhao. "StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis". Proceedings of the AAAI Conference on Artificial Intelligence 38, n. 17 (24 marzo 2024): 19597–605. http://dx.doi.org/10.1609/aaai.v38i17.29932.
Abstract (sommario):
Style transfer for out-of-domain (OOD) singing voice synthesis (SVS) focuses on generating high-quality singing voices with unseen styles (such as timbre, emotion, pronunciation, and articulation skills) derived from reference singing voice samples. However, the endeavor to model the intricate nuances of singing voice styles is an arduous task, as singing voices possess a remarkable degree of expressiveness. Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase. To overcome these challenges, we propose StyleSinger, the first singing voice synthesis model for zero-shot style transfer of out-of-domain reference singing voice samples. StyleSinger incorporates two critical approaches for enhanced effectiveness: 1) the Residual Style Adaptor (RSA) which employs a residual quantization module to capture diverse style characteristics in singing voices, and 2) the Uncertainty Modeling Layer Normalization (UMLN) to perturb the style attributes within the content representation during the training phase and thus improve the model generalization. Our extensive evaluations in zero-shot style transfer undeniably establish that StyleSinger outperforms baseline models in both audio quality and similarity to the reference singing voice samples. Access to singing voice samples can be found at https://stylesinger.github.io/.
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Akber, Syed Muhammad Abrar, Sadia Nishat Kazmi, Syed Muhammad Mohsin e Agnieszka Szczęsna. "Deep Learning-Based Motion Style Transfer Tools, Techniques and Future Challenges". Sensors 23, n. 5 (26 febbraio 2023): 2597. http://dx.doi.org/10.3390/s23052597.
Abstract (sommario):
In the fourth industrial revolution, the scale of execution for interactive applications increased substantially. These interactive and animated applications are human-centric, and the representation of human motion is unavoidable, making the representation of human motions ubiquitous. Animators strive to computationally process human motion in a way that the motions appear realistic in animated applications. Motion style transfer is an attractive technique that is widely used to create realistic motions in near real-time. motion style transfer approach employs existing captured motion data to generate realistic samples automatically and updates the motion data accordingly. This approach eliminates the need for handcrafted motions from scratch for every frame. The popularity of deep learning (DL) algorithms reshapes motion style transfer approaches, as such algorithms can predict subsequent motion styles. The majority of motion style transfer approaches use different variants of deep neural networks (DNNs) to accomplish motion style transfer approaches. This paper provides a comprehensive comparative analysis of existing state-of-the-art DL-based motion style transfer approaches. The enabling technologies that facilitate motion style transfer approaches are briefly presented in this paper. When employing DL-based methods for motion style transfer, the selection of the training dataset plays a key role in the performance. By anticipating this vital aspect, this paper provides a detailed summary of existing well-known motion datasets. As an outcome of the extensive overview of the domain, this paper highlights the contemporary challenges faced by motion style transfer approaches.
36
Cao, Deborah. "Strategies in Translating Oral History Between Chinese and English". Babel. Revue internationale de la traduction / International Journal of Translation 40, n. 3 (1 gennaio 1994): 137–45. http://dx.doi.org/10.1075/babel.40.3.03cao.
Abstract (sommario):
L'article se penche sur les traductions en anglais et en chinois de la tradition orale en se référant plus particulièrement aux traductions en anglais de la tradition orale Chinoise, Beijingren, de Zhang Xinxin et Sang Ye, et à la traduction en chinois de la tradition orale anglaise, American Dreams, par Studs Turkel. Par ailleurs, l'article propose des stratégies permettant de traduire ce genre littéraire dans deux langues aussi éloignées l'une de l'autre que sont l'anglais et le chinois. Il défend la thèse que ce sont les aspects linguistiques et non linguistiques de la tradition orale qui doivent l'emporter sur toute autre considération pour déterminer la méthode de transfert et les stratégies de traduction à utiliser. Dans son étude, l'auteur identifie trois problématiques principales d'ordre linguistique et sociolinguistique, dignes de susciter l'intérêt du traducteur amené à traduire la tradition orale. Il s'agit de la syntaxe, de la lexicologie et de l'emploi des varietés linguistiques dans la traduction. L'article aborde le problème des différences entre la langue écrite et parlée, entre les dialectes et les registres en traduction. Il fait remarquer que ces problèmes son liés à l'énorme différence qu'il y a entre l'anglais et le chinois, aussi bien du point de vue linguistique que sociolinguistique, et aux aspects spécifiques de la tradition orale. L'auteur suggère des moyens pour rendre plus efficace la traduction de ce genre de textes. Il propose d'activer différentes astuces linguistiques aussi bien au niveau linguistique que sociolinguistique, en vue de ratteindre le style particulier de la tradition orale.
37
Zhang, Chi, Yixin Zhu e Song-Chun Zhu. "MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 1254–61. http://dx.doi.org/10.1609/aaai.v33i01.33011254.
Abstract (sommario):
An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method. One of the remaining challenges is to balance a trade-off among three critical aspects—speed, flexibility, and quality: (i) the vanilla optimization-based algorithm produces impressive results for arbitrary styles, but is unsatisfyingly slow due to its iterative nature, (ii) the fast approximation methods based on feed-forward neural networks generate satisfactory artistic effects but bound to only a limited number of styles, and (iii) feature-matching methods like AdaIN achieve arbitrary style transfer in a real-time manner but at a cost of the compromised quality. We find it considerably difficult to balance the trade-off well merely using a single feed-forward step and ask, instead, whether there exists an algorithm that could adapt quickly to any style, while the adapted model maintains high efficiency and good image quality. Motivated by this idea, we propose a novel method, coined MetaStyle, which formulates the neural style transfer as a bilevel optimization problem and combines learning with only a few post-processing update steps to adapt to a fast approximation model with satisfying artistic effects, comparable to the optimization-based methods for an arbitrary style. The qualitative and quantitative analysis in the experiments demonstrates that the proposed approach achieves high-quality arbitrary artistic style transfer effectively, with a good trade-off among speed, flexibility, and quality.
38
Wu, Zhijie, Chunjin Song, Yang Zhou, Minglun Gong e Hui Huang. "EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 07 (3 aprile 2020): 12305–12. http://dx.doi.org/10.1609/aaai.v34i07.6914.
Abstract (sommario):
Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimization in the deep feature space, quite a few approaches have achieved real-time arbitrary style transfer with straightforward statistic matching techniques. In this work, our key observation is that only considering features in the input style image for the global deep feature statistic matching or local patch swap may not always ensure a satisfactory style transfer; see e.g., Figure 1. Instead, we propose a novel transfer framework, EFANet, that aims to jointly analyze and better align exchangeable features extracted from the content and style image pair. In this way, the style feature from the style image seeks for the best compatibility with the content information in the content image, leading to more structured stylization results. In addition, a new whitening loss is developed for purifying the computed content features and better fusion with styles in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.
39
Liu, Xueting, Wenliang Wu, Huisi Wu e Zhenkun Wen. "Deep Style Transfer for Line Drawings". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 1 (18 maggio 2021): 353–61. http://dx.doi.org/10.1609/aaai.v35i1.16111.
Abstract (sommario):
Line drawings are frequently used to illustrate ideas and concepts in digital documents and presentations. To compose a line drawing, it is common for users to retrieve multiple line drawings from the Internet and combine them as one image. However, different line drawings may have different line styles and are visually inconsistent when put together. In order that the line drawings can have consistent looks, in this paper, we make the first attempt to perform style transfer for line drawings. The key of our design lies in the fact that centerline plays a very important role in preserving line topology and extracting style features. With this finding, we propose to formulate the style transfer problem as a centerline stylization problem and solve it via a novel style-guided image-to-image translation network. Results and statistics show that our method significantly outperforms the existing methods both visually and quantitatively.
40
Buwah, Neba Noela, Ibrahima Ibrahima, Negou Ernest e Elangwe Eugenia Bie. "Leadership Styles and Organisational Performance: Case of Small Businesses in Cameroon Size Businesses in Buea Municipality". International Journal of Small Business and Entrepreneurship Research 12, n. 1 (15 gennaio 2024): 24–46. http://dx.doi.org/10.37745/ijsber.2013/vol12n12446.
Abstract (sommario):
This study assesses the relationship between leadership styles and organisational performance for small and medium size organisations in Cameroon. Specifically, it examines the effect of authoritarian leadership style on organisational performance, the effect of democratic leadership style on organisational performance and the effect of transformational leadership style on organiational performance. Using a causal research design, the research selected 100 employees in small and medium size organisations through purposive sampling technique. Descriptive and inferential statistics were used through SPSS version 25 to analyse data. Findings reveal that there is a positive and significant relationship between leadership styles and organizational performance in small businesses studied. Results further reveal that democratic leadership styles do not have a positive effect on organizational performance while authoritarian and transformational leadership style has a positive and significant impact on organisational performance. It was recommended that managers in small and medium size organisations use authoritarian and transformational leadership styles in order to increase performance.
41
Ren, Yeqing, Youqiang Sun, Xuechun Jing, Zhihua Cui e Zhentao Shi. "Adaptive Makeup Transfer via Bat Algorithm". Mathematics 7, n. 3 (17 marzo 2019): 273. http://dx.doi.org/10.3390/math7030273.
Abstract (sommario):
With the advent of the artificial intelligence (AI) era, the beauty camera is widely used, and makeup transfer has attracted increasing attention. In this paper, we propose an adaptive makeup transfer based on the bat algorithm to solve the problem that only a single makeup effect can be transferred. According to the characteristics of makeup style, the algorithm optimizes the weight value to get the appropriate makeup lightness by using the adaptive method. The improved algorithm can not only help to get the optimal weight values in the process of transferring the same makeup style to different targets, but also to transfer different makeup styles to the same target. Moreover, this algorithm can choose the most suitable makeup style and also the most appropriate lightness for a certain person. Experimental results show that the algorithm proposed in the paper has a better effect than the existing algorithm of makeup transfer, and the algorithm can provide users with a suitable makeup style and appropriate lightness.
42
Ling, Xufeng, Yu Zhu, Wei Liu, Jingxin Liang e Jie Yang. "The Generation of Articulatory Animations Based on Keypoint Detection and Motion Transfer Combined with Image Style Transfer". Computers 12, n. 8 (28 luglio 2023): 150. http://dx.doi.org/10.3390/computers12080150.
Abstract (sommario):
Knowing the correct positioning of the tongue and mouth for pronunciation is crucial for learning English pronunciation correctly. Articulatory animation is an effective way to address the above task and helpful to English learners. However, articulatory animations are all traditionally hand-drawn. Different situations require varying animation styles, so a comprehensive redraw of all the articulatory animations is necessary. To address this issue, we developed a method for the automatic generation of articulatory animations using a deep learning system. Our method leverages an automatic keypoint-based detection network, a motion transfer network, and a style transfer network to generate a series of articulatory animations that adhere to the desired style. By inputting a target-style articulation image, our system is capable of producing animations with the desired characteristics. We created a dataset of articulation images and animations from public sources, including the International Phonetic Association (IPA), to establish our articulation image animation dataset. We performed preprocessing on the articulation images by segmenting them into distinct areas each corresponding to a specific articulatory part, such as the tongue, upper jaw, lower jaw, soft palate, and vocal cords. We trained a deep neural network model capable of automatically detecting the keypoints in typical articulation images. Also, we trained a generative adversarial network (GAN) model that can generate end-to-end animation of different styles automatically from the characteristics of keypoints and the learned image style. To train a relatively robust model, we used four different style videos: one magnetic resonance imaging (MRI) articulatory video and three hand-drawn videos. For further applications, we combined the consonant and vowel animations together to generate a syllable animation and the animation of a word consisting of many syllables. Experiments show that this system can auto-generate articulatory animations according to input phonetic symbols and should be helpful to people for English articulation correction.
43
Hien, Ngo Le Huy, Luu Van Huy e Nguyen Van Hieu. "Artwork style transfer model using deep learning approach". Cybernetics and Physics, Volume 10, 2021, Number 3 (30 ottobre 2021): 127–37. http://dx.doi.org/10.35470/2226-4116-2021-10-3-127-137.
Abstract (sommario):
Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.
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Wu, Jingyu, Lefan Hou, Zejian Li, Jun Liao, Li Liu e Lingyun Sun. "Preserving Structural Consistency in Arbitrary Artist and Artwork Style Transfer". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 3 (26 giugno 2023): 2830–38. http://dx.doi.org/10.1609/aaai.v37i3.25384.
Abstract (sommario):
Deep generative models are effective in style transfer. Previous methods learn one or several specific artist-style from a collection of artworks. These methods not only homogenize the artist-style of different artworks of the same artist but also lack generalization for the unseen artists. To solve these challenges, we propose a double-style transferring module (DSTM). It extracts different artist-style and artwork-style from different artworks (even untrained) and preserves the intrinsic diversity between different artworks of the same artist. DSTM swaps the two styles in the adversarial training and encourages realistic image generation given arbitrary style combinations. However, learning style from single artwork can often cause over-adaption to it, resulting in the introduction of structural features of style image. We further propose an edge enhancing module (EEM) which derives edge information from multi-scale and multi-level features to enhance structural consistency. We broadly evaluate our method across six large-scale benchmark datasets. Empirical results show that our method achieves arbitrary artist-style and artwork-style extraction from a single artwork, and effectively avoids introducing the style image’s structural features. Our method improves the state-of-the-art deception rate from 58.9% to 67.2% and the average FID from 48.74 to 42.83.
45
Kim, Kyeongseon, e Joongheon Kim. "Style Transfer Deep Learning Framework for Nighttime Robust Vehicle Detection in On-Road Mobile Platforms". Journal of KIISE 46, n. 9 (30 settembre 2019): 968–73. http://dx.doi.org/10.5626/jok.2019.46.9.968.
46
Cui, Xin, Meng Qi, Yi Niu e Bingxin Li. "The Intra-Class and Inter-Class Relationships in Style Transfer". Applied Sciences 8, n. 9 (17 settembre 2018): 1681. http://dx.doi.org/10.3390/app8091681.
Abstract (sommario):
Neural style transfer, which has attracted great attention in both academic research and industrial engineering and demonstrated very exciting and remarkable results, is the technique of migrating the semantic content of one image to different artistic styles by using convolutional neural network (CNN). Recently, the Gram matrices used in the original and follow-up studies for style transfer were theoretically shown to be equivalent to minimizing a specific Maximum Mean Discrepancy (MMD). Since the Gram matrices are not a must for style transfer, how to design the proper process for aligning the neural activation between images to perform style transfer is an important problem. After careful analysis of some different algorithms for style loss construction, we discovered that some algorithms consider the relationships between different feature maps of a layer obtained from the CNN (inter-class relationships), while some do not (intra-class relationships). Surprisingly, the latter often show more details and finer strokes in the results. To further support our standpoint, we propose two new methods to perform style transfer: one takes inter-class relationships into account and the other does not, and conduct comparative experiments with existing methods. The experimental results verified our observation. Our proposed methods can achieve comparable perceptual quality yet with a lower complexity. We believe that our interpretation provides an effective design basis for designing style loss function for style transfer methods with different visual effects.
47
Kim, Sunwoo, Soohyun Kim e Seungryong Kim. "Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer". Proceedings of the AAAI Conference on Artificial Intelligence 36, n. 1 (28 giugno 2022): 1183–91. http://dx.doi.org/10.1609/aaai.v36i1.20004.
Abstract (sommario):
Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such test-time training for style transfer, we present novel network architectures, with two sub-modules of correspondence and generation modules, and loss functions consisting of contrastive content, style, and cycle consistency losses. Our framework does not require offline training phase for style transfer, which has been one of the main challenges in existing methods, but the networks are to be solely learned during test time. Experimental results prove that our framework has a better generalization ability to unseen image pairs and even outperforms the state-of-the-art methods.
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Wang, Qi, e Alamusi H. "Design of Watercolor Cultural and Creative Products Based on Style Transfer Algorithm". Mathematical Problems in Engineering 2022 (31 luglio 2022): 1–9. http://dx.doi.org/10.1155/2022/2711861.
Abstract (sommario):
Due to the long design cycle of watercolor cultural and creative products, a design method based on style transfer algorithm is proposed. Here, the design of Shanghai style watercolor painting cultural and creative products is taken as the research object, VGG-19 network is used as the style transfer model, and the loss function of style transfer algorithm is designed. The simulation results show that the proposed method can effectively extract and reconstruct the color, texture, brushstroke, and other styles of Shanghai style watercolor painting. The generated new images are added as design elements, which makes the cultural and creative products have both appearance and personalization. In addition, the newly generated cultural and creative products have also obtained high satisfaction from experts and users, which meet the aesthetic changes. It can be seen that proposed method provides a new idea for the design of watercolor cultural and creative products, which shortens the product design cycle, reduces the design cost, and has a certain practicality.
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Wu Meng, 吴萌, 高怡宁 Gao Yining e 王佳 Wang Jia. "结合特征聚类和深度残差收缩网络的壁画风格迁移". Laser & Optoelectronics Progress 60, n. 16 (2023): 1633001. http://dx.doi.org/10.3788/lop222583.
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Ayu Wulandari, Dewi, Choirul Huda e Sri Suneki. "ANALISIS GAYA BELAJAR PESERTA DIDIK PADA PEMBELAJARAN MATEMATIKA KELAS 1 DI SD NEGERI PETOMPON 02 SEMARANG". Didaktik : Jurnal Ilmiah PGSD STKIP Subang 9, n. 2 (28 giugno 2023): 2808–33. http://dx.doi.org/10.36989/didaktik.v9i2.1160.
Abstract (sommario):
Students' learning styles in learning vary, so it is important for teachers to know the learning styles of their students. There are students who feel that mathematics is difficult to understand and understand, and teachers do not know student learning styles. One of the characteristics of learning that is related to the acquisition, processing, and transfer of knowledge is the student's learning style. The purpose of this study is to determine students' learning styles when learning mathematics. Learning style is a very important form of learning. Information related to the characteristics of student learning styles, it is very important for teachers to improve the quality of learning. The research method is qualitative research. Data collection techniques through interviews, observation and documentation. Data validity was carried out through technique and source triangulation. The data analysis technique used consists of data acquisition, data reduction, data presentation and drawing conclusions. The results showed that the learning style of grade 1 students at SD Negeri Petompon 02 Semarang in learning mathematics, which showed visual characteristics had visual characteristics, namely students could understand problems by reading, which they themselves could not. The auditory learning style means that students understand better when the teacher reads the questions, knows how to convey information obtained from their work orally. A learning style with kinesthetic characteristics, namely students prefer to solve problems in groups, cannot concentrate on solving problems if they have to sit still for a long time. The results showed that the learning style of grade 1 students at SD Negeri Petompon 02 Semarang was a learning style. visual, auditory, kinesthetic and the most dominant learning style is the kinesthetic learning style.