Transformation of portraits to Picasso’s cubism style

Lian, Guanyu; Kang, Zhuang

doi:10.1007/s00371-019-01661-2

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Therefore, the style image cannot be selected freely. So far, after several years of development, neural style transfer has been applied to many fields, such as fashion [18], photography [19], portrait [20], music [21] etc.…”

Section: Neural Style Transfermentioning

confidence: 99%

Style transfer network for complex multi-stroke text

Chen

Wang

et al. 2023

Multimedia Systems

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Neural style transfer has achieved success in many tasks. It is also introduced to text style transfer, which uses a style image to generate transferred images with textures and shapes consistent with the semantic content of the reference image. However, when the text structure is complex, existing methods will encounter problems such as stroke adhesion and unclear text edges. This will affect the aesthetics of the generated image and bring a lot of extra workload to the designers. This paper proposes an improved text style transfer network for complex multistroke texts. We use Shape-matching Generative Adversarial Network (GAN) as the baseline network and perform the following modifications: (1) Morphological methods, erosion and dilation, are introduced in image processing; (2) Residual modules and loss functions are added to the network; and (3) AdaBelief optimizer is adopted to constrain the transfer of text structure. Further, a new dataset of traditional Chinese characters is constructed to train the model. Experimental results show that the proposed method outperforms state-of-the-art methods on both simple characters and complex multi-stroke characters. It is shown that our method increases the readability and aesthetics of the text.

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Section: Neural Style Transfermentioning

confidence: 99%

Style transfer network for complex multi-stroke text

Chen

Wang

et al. 2023

Multimedia Systems

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“…Gatys et al [156] demonstrated that visual features of models could be combined to represent image styles. It arises in a context of strong growth in DNNs for several applications, including art and painting [157,158]. For example, Lian et al [157] proposed a style transfer-based method that takes any natural portrait of a human and transforms it into Picasso's cubism style.…”

Section: Style Transfermentioning

confidence: 99%

“…It arises in a context of strong growth in DNNs for several applications, including art and painting [157,158]. For example, Lian et al [157] proposed a style transfer-based method that takes any natural portrait of a human and transforms it into Picasso's cubism style. Informally, style transfer is an optimization-based technique that renders the content of an existing image (content image) in the style of another image (style image).…”

Section: Style Transfermentioning

confidence: 99%

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

et al. 2021

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The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research.

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