Hasil untuk "Style. Composition. Rhetoric"

Ian Jaffray, John Bronskill

We present a novel, regression-based method for artistically styling images. Unlike recent neural style transfer or diffusion-based approaches, our method allows for explicit control over the stroke composition and level of detail in the rendered image through the use of an extensible set of stroke patches. The stroke patch sets are procedurally generated by small programs that control the shape, size, orientation, density, color, and noise level of the strokes in the individual patches. Once trained on a set of stroke patches, a U-Net based regression model can render any input image in a variety of distinct, evocative and customizable styles.

Jonatan Horvat

Review of Richard Müller, The Emerging Contours of the Medium: Literature and Mediality (Bloomsbury, 2024).

Riccardo Iannaccone

Questo lavoro analizza la Nationality and Entry into Israel Law come strumento di costruzione normativa della cittadinanza e di esercizio del potere politico in Israele. Sebbene giustificata ufficialmente per motivi di sicurezza nazionale, la legge risponde anche a obiettivi demografici: contenere l’unificazione familiare tra cittadini palestinesi israeliani e residenti dei Palestinian Occupied Territories per preservare la maggioranza ebraica. Tuttavia, tale finalità non è dichiarata esplicitamente, per timore che non sia compatibile con la Basic Law: Human Dignity and Liberty. La Nation-State Law del 2018 ha offerto una cornice etnonazionale a sostegno della legge, senza però risolvere il conflitto con il principio di uguaglianza. Il testo mostra come le istituzioni contribuiscano alla costruzione di una ‘verità’ ufficiale incentrata sulla sicurezza, mentre l’obiettivo demografico opera implicitamente, producendo effetti concreti di esclusione. La legge diventa così un dispositivo politico che definisce chi può appartenere alla nazione e chi ne resta ai margini.

Ye Wang, Tongyuan Bai, Xuping Xie et al.

Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despite numerous advances in this field, existing methods did not explicitly focus on the signature style, which represents the distinct and recognizable visual traits of the image such as geometric and structural patterns, color palettes and brush strokes etc. In this paper, we introduce SigStyle, a framework that leverages the semantic priors that embedded in a personalized text-to-image diffusion model to capture the signature style representation. This style capture process is powered by a hypernetwork that efficiently fine-tunes the diffusion model for any given single style image. Style transfer then is conceptualized as the reconstruction process of content image through learned style tokens from the personalized diffusion model. Additionally, to ensure the content consistency throughout the style transfer process, we introduce a time-aware attention swapping technique that incorporates content information from the original image into the early denoising steps of target image generation. Beyond enabling high-quality signature style transfer across a wide range of styles, SigStyle supports multiple interesting applications, such as local style transfer, texture transfer, style fusion and style-guided text-to-image generation. Quantitative and qualitative evaluations demonstrate our approach outperforms existing style transfer methods for recognizing and transferring the signature styles.

Gur Elkin, Ofir Itzhak Shahar, Yaniv Ohayon et al.

Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its source, and pieces from different objects being mixed, categorizing broken artifacts based on their visual cues could be a challenging task, even for professionals. As classification is a common function of many machine learning models, the power of modern architectures can be harnessed for efficient and accurate fragment classification. In this work, we present a generalized deep-learning framework for predicting the artistic style of image fragments, achieving state-of-the-art results for pieces with varying styles and geometries.

Bohumil Fořt

This study examines the temporal and spatial qualities associated with realist and naturalist novels in general and with Émile Zola’s Germinal in particular. It is based on the assumption that the spatial-temporal qualities of both realist and naturalist genres are fundamentally determined by the pragmatic aspects of literary realism and naturalism, as outlined by early realist thinkers and writers such as Guy de Maupassant, George Eliot, Gustav Flaubert, the de Goncourt brothers, Henry James, Émile Zola, and others. The study aims to provide a deeper understanding of spatial-temporal qualities and the ways in which they contribute to the genre’s distinctive style and impact. By examining these qualities in detail, the paper sheds light on the vital role that naturalist novels play in reflecting and shaping our understanding of the world. The study uses Émile Zola’s Germinal (1885) as a primary referential framework to exemplify the theoretical findings.

Robin Gerster, Holger Caesar, Matthias Rapp et al.

Despite their success in various vision tasks, deep neural network architectures often underperform in out-of-distribution scenarios due to the difference between training and target domain style. To address this limitation, we introduce One-Shot Style Adaptation (OSSA), a novel unsupervised domain adaptation method for object detection that utilizes a single, unlabeled target image to approximate the target domain style. Specifically, OSSA generates diverse target styles by perturbing the style statistics derived from a single target image and then applies these styles to a labeled source dataset at the feature level using Adaptive Instance Normalization (AdaIN). Extensive experiments show that OSSA establishes a new state-of-the-art among one-shot domain adaptation methods by a significant margin, and in some cases, even outperforms strong baselines that use thousands of unlabeled target images. By applying OSSA in various scenarios, including weather, simulated-to-real (sim2real), and visual-to-thermal adaptations, our study explores the overarching significance of the style gap in these contexts. OSSA's simplicity and efficiency allow easy integration into existing frameworks, providing a potentially viable solution for practical applications with limited data availability. Code is available at https://github.com/RobinGerster7/OSSA

Davide Belgradi

L’articolo propone lo studio di Mano rubata di Tommaso Landolfi, racconto che l’autore dedica a una delle sue ossessioni, il gioco d’azzardo. Il gioco, in questo caso potenzialmente mortale, viene analizzato dapprima secondo le categorie di agon, alea e ilinx di Caillois. La scommessa mortale, però, è studiata da una prospettiva teorica specificamente lacaniana. Non si tratta di attribuire a Landolfi un’indebita adesione alle teorie psicanalitiche, quanto di servirsi di un grimaldello teorico laddove testo e teoria sembrano incontrarsi naturalmente su un campo neutro. Il ‘campo neutro’ è quello dello sguardo: più precisamente di uno sguardo voyeuristico. Si noterà come, dietro alla scommessa tra Gisa e Marcello, ci sia lo sguardo voyeuristico e sovraindividuale del grande Altro, che condannerebbe la donna a soggiacere a una struttura di potere. Di fronte a questa eventualità, l’accettazione del suicidio permette a Gisa di denunciare e disinnescare l’intima violenza dello sguardo.

Francisco Sánchez Torres

La primera condena a los libros De Rege et Regis institutione por parte de la Sorbona, con fecha de 4 de junio de 1610, se produjo en un contexto de frenética producción propagandística e intelectual que tuvo como objetivo desprestigiar a la Compañía de Jesús. A raíz de la presunta involucración de algunos de sus miembros en hechos como los intentos fallidos de asesinato de Enrique IV de Francia o, posteriormente, el intento que sí tuvo éxito, numerosos panfletos y otros textos usaron las palabras de Mariana como causa de acusación principal. También se echó manos de otros autores como Ribadeneira o Belarmino. La intervención de Pierre Coton con su Lettre déclaratoire no apaciguó a sus enemigos, sino que avivó el ya encendido debate. La respuesta más destacada fue el Anticoton, que obtuvo respuesta por parte de un padre jesuita no identificado en la Response apologetique à l’Anticoton. Tal respuesta obligó a la Sorbona a reunirse de nuevo para decretar una segunda condena a Mariana y una primera a este texto. Este trabajo transcribe la censura publicada por la Sorbona el 1 de febrero de 1611.

Kibeom Hong, Seogkyu Jeon, Junsoo Lee et al.

To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific local patches from the style image, resulting in disharmonious and evident repetitive artifacts. To overcome this limitation and accomplish impeccable artistic style transfer, we focus on enhancing the attention mechanism and capturing the rhythm of patterns that organize the style. In this paper, we introduce a novel metric, namely pattern repeatability, that quantifies the repetition of patterns in the style image. Based on the pattern repeatability, we propose Aesthetic Pattern-Aware style transfer Networks (AesPA-Net) that discover the sweet spot of local and global style expressions. In addition, we propose a novel self-supervisory task to encourage the attention mechanism to learn precise and meaningful semantic correspondence. Lastly, we introduce the patch-wise style loss to transfer the elaborate rhythm of local patterns. Through qualitative and quantitative evaluations, we verify the reliability of the proposed pattern repeatability that aligns with human perception, and demonstrate the superiority of the proposed framework.

Weijian Ma, Yanyang Kong

Artistic style transfer aims to modify the style of the image while preserving its content. Style transfer using deep learning models has been widely studied since 2015, and most of the applications are focused on specific artists like Van Gogh, Monet, Cezanne. There are few researches and applications on traditional Chinese painting style transfer. In this paper, we will study and leverage different state-of-the-art deep generative models for Chinese painting style transfer and evaluate the performance both qualitatively and quantitatively. In addition, we propose our own algorithm that combines several style transfer models for our task. Specifically, we will transfer two main types of traditional Chinese painting style, known as "Gong-bi" and "Shui-mo" (to modern images like nature objects, portraits and landscapes.

Ming Gao, YanWu Xu, Yang Zhao et al.

In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler). We aim at stylizing any 3D scene with an arbitrary style from a text description, and synthesizing the novel stylized view, which is more flexible than the image-conditioned style transfer. Compared with the previous 2D method CLIPStyler, we are able to stylize a 3D scene and generalize to novel scenes without re-train our model. A straightforward solution is to combine previous image-conditioned 3D style transfer and text-conditioned 2D style transfer \bigskip methods. However, such a solution cannot achieve our goal due to two main challenges. First, there is no multi-modal model matching point clouds and language at different feature scales (low-level, high-level). Second, we observe a style mixing issue when we stylize the content with different style conditions from text prompts. To address the first issue, we propose a 3D stylization framework to match the point cloud features with text features in local and global views. For the second issue, we propose an improved directional divergence loss to make arbitrary text styles more distinguishable as a complement to our framework. We conduct extensive experiments to show the effectiveness of our model on text-guided 3D scene style transfer.

Zhanjie Zhang, Quanwei Zhang, Guangyuan Li et al.

Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. Small model-based approaches can preserve the content strucuture, but fail to produce highly realistic stylized images and introduce artifacts and disharmonious patterns; Pre-trained large-scale model-based approaches can generate highly realistic stylized images but struggle with preserving the content structure. To address the above issues, we propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images while preserving the content structure of the content images. Specifically, to sufficiently dig out the knowledge embedded in pre-trained large-scale models, an Implicit Style Prompt Bank (ISPB), a set of trainable parameter matrices, is designed to learn and store knowledge from the collection of artworks and behave as a visual prompt to guide pre-trained large-scale models to generate highly realistic stylized images while preserving content structure. Besides, to accelerate training the above ISPB, we propose a novel Spatial-Statistical-based self-Attention Module (SSAM). The qualitative and quantitative experiments demonstrate the superiority of our proposed method over state-of-the-art artistic style transfer methods.

Xiao He, Mingrui Zhu, Nannan Wang et al.

Few-shot font generation (FFG) aims to preserve the underlying global structure of the original character while generating target fonts by referring to a few samples. It has been applied to font library creation, a personalized signature, and other scenarios. Existing FFG methods explicitly disentangle content and style of reference glyphs universally or component-wisely. However, they ignore the difference between glyphs in different styles and the similarity of glyphs in the same style, which results in artifacts such as local distortions and style inconsistency. To address this issue, we propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font). We introduce contrastive learning to consider the positive and negative relationship between styles. Specifically, we propose a multi-layer style projector for style encoding and realize a distinctive style representation via our proposed Cluster-level Contrastive Style (CCS) loss. In addition, we design a multi-task patch discriminator, which comprehensively considers different areas of the image and ensures that each style can be distinguished independently. We conduct qualitative and quantitative evaluations comprehensively to demonstrate that our approach achieves significantly better results than state-of-the-art methods.

Romain Benini

Maria Saltykov

Francisco de Asís Palomo Ruano

The main aim of this paper is to bring out and depict the various rhetoric and style elements in Richard de Fournival's Bestiaire d'Amour prologue, also dealing with the question of marking off the boundaries of such a literary excerpt, both formally and semantically, as the Bestiaire d'Amour itself represents a special, innovative work which requires a specific introduction, too. Far from the previous medieval bestiaries and other similar compositions whose incipit, if existent, usually focused either on the divine invocation or likely kept up to feudal bonds, our prologue is built upon a coalescence of religious, artistic, moral, amatory and military values, not to mention vassal relationships. The whole is rightly structured by means of several assets and techniques dating back to classical Rhetoric but kept alive in Middle Ages as they were widely implemented in literary compositions. El objetivo principal de este artículo consiste en extraer y describir los variados elementos retóricos y estilísticos del prólogo al Bestiaire d'Amour de Richard de Fournival, así como considerar la problemática de especificar los límites de dicho fragmento literario tanto formal como semánticamente, pues el Bestiaire d'Amour representa una obra tan especial e innovadora que demanda asimismo una introducción específica. Alejado de otros bestiarios medievales anteriores así como de otras composiciones similares cuyo íncipit, si existía, se centraba en la invocación divina o se mantenía aparentemente ligada a relaciones feudales, nuestro prólogo se construye sobre una fusión de elementos religiosos, artísticos, morales, amorosos o militares, sin mencionar las relaciones vasalláticas. Todo ello está adecuadamente estructurado por medio de elementos y técnicas propias de la Retórica clásica, pero mantenidas al día en la Edad Media ya que se implementaban constantemente en las composiciones literarias.

Juguang Meng

The subject of this research is the trumpet parts in Henry Purcell’s "Ode on St. Cecilia`s Day" (1692). The author analyses the role of the trumpet in the context of the general content of the work and its poetic symbolism. In this regard, the issue of using special compositional techniques, in particular sound visualisation and musical rhetoric, is analysed. In addition, questions of trumpet tonality semantics are considered. A large amount of background information on the history of the music holiday origin is provided in the article; opinions of scientists who put forward hypotheses about the fruitfulness of the English cultural soil for the rooting and development of such celebrations are given. The author mentions the names of outstanding trumpeters for whom trumpet parts were created, and whose art served as the foundation for the development of the trumpet style in England in the 17th-18th centuries. Along with the performers who made Purcell's art famous, information on fellow composers, Blow and Draghi, is provided; they also created musical offerings to St. Cecilia and undoubtedly influenced the style of young Purcell. The author believes that a new approach to this instrument’s capabilities and the presence of qualified performers broadened the area for the composer's fantasies and experiments. It was reflected not only in the creation of countless motifs based on fanfare and trumpet trill, expansion of the sound range and the use of “imperfect” harmonics of the natural scale but also made it possible to assign to the trumpet repertoire a certain intonation vocabulary, easily perceived, recognizable and freely used in vocal and various instrumental parts. The author comes to the conclusion that Purcell perceived the trumpet as an instrument with great virtuoso and cantilena potential; he used it in climactic moments, maintaining absolute balance with the winds and strings. With Purcell's work, the future of trumpet music became more distinct. Keywords: natural trumpet, Ode to St. Cecilia, English Baroque.

Nathan DeProspo

In this article, I will attempt an unbuilding of a history of composition—a history of policy in/as language—to see how claims of policy (pedagogical, historiographical, conceptual) influence, complicate, or even reverse the direction of certain theoretical projects in rhetoric and composition

Meihao Kong, Jing Huo, Wenbin Li et al.

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models.