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Rethink arbitrary style transfer with transformer and contrastive ...


Image and Video Style Transfer Based on Transformer (March 2023)

The algorithm uses neural networks to separate and reorganize the content and style of arbitrary images. However, the iterative optimization process of this ...

An improved defocusing adaptive style transfer method based on a ...

In contrast, a deep network with a larger receptive field is better at learning to draw large strokes. Since the output of each branch is the ...

Learning Graph Neural Networks for Image Style Transfer

By contrast, the proposed. GNN-based semi-parametric approach achieves superior stylization performance in the transfers of both global stroke arrangement and ...

BENDR: Using Transformers and a Contrastive Self-Supervised ...

We first begin by investigating fully supervised transfer learning (which has been frequently looked to as an EEG/BCI TL solution), finding inconsistency in the ...

Adversarial Unsupervised Style Transfer using a Pretrained Model

Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model. Hojun Cho1, Dohee Kim1 ...

GANST: Gradient-aware Arbitrary Neural Style Transfer - Eurographics

In contrast, GANST produces results with concrete styles reflected and detailed content preserved. Ablation Study with GANST Variants. To better evaluate ...

Vision transformer - Wikipedia

A vision transformer (ViT) is a transformer designed for computer vision. A ViT decomposes an input image into a series of patches serializes each patch ...

Total Style Transfer with a Single Feed-Forward Network - MDPI

The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style.

ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

Fast patch-based style transfer of arbitrary style. arXiv preprint arXiv ... Rethinking image inpainting via a mutual encoder- decoder with feature equalizations.