# Feature Separation and Fusion to Optimise the Migration Model of Mural Painting Style in Toms
## 2 Related Work
### Style Feature Style Migration  
Feed-forward neural networks based on the research of [PERSON] et al. are being used extensively to handle style migration tasks. Various transform-based methods implementing encoder-decoder architecture have been proposed. The AdaIN [19] technique is one of the most traditional methods that uses an adaptive instance normalisation layer to derive the mean and variance of features, thus enabling the module to learn spatial features from both shallow and deep features. Building upon AdaIN, the Whitening and Colouring Transform (WCT) [20] optimises by replacing variance with covariance. OptimalWCT [21] further improves results by utilizing a more general closed-form solution. Similarly, Linear Style Transfer (LST) [22] proposes a linear transformation of cross-domain features to solve the generic style migration problem. Additionally, ArtFlow [23] implements reversible neural flow to solve the problem of content leakage during transformation. Additionally, another approach to solving the image style transformation problem is the patch-based approach, where pattern features are used to beautify content features [24]. Specifically, StyleSwap [25] substitutes each activation patch of the content image with a corresponding style patch.  
With the widespread use of attention mechanisms [26], attention-based methods have been introduced to the style migration field, resulting in a variety of attention-based models [27]. Firstly, when computing attention towards style within the feature space, taking into account the necessity of extracting features from various layers, SANet [28]merges both local and global style patterns to incorporate style features that match the content features. Based on SANet model features, IEContraAST [29] can amalgamate internal and external learning [30], and comparative learning [31]. It uses SANet as its backbone and learns to incorporate human-perceived styles through the discriminator's external learning [32] to ensure precise content and style delivery. Furthermore, MANet [33] and mcnet [27] utilise multi-adaptive and multi-channel correlation techniques, respectively, to enhance feature fusion performance. PAMA [34] introduces progressive attentional manifold alignment, which relocates style features dynamically using repetitive attentional operations. Furthermore, within the style migration domain, there have been proposed conversion methods that utilise self-attention mechanisms and positional encoding [35]. With regards to the style transfer task, [PERSON] [36] has modified the converter structure and suggested the implementation of content-aware positional encoding. To ensure optimal style transformation, StyleFormer [37] has modified the transformer structure by utilising a combination of style libraries and parameterisation. Additionally, [PERSON] et al. [38] have proposed a novel form of adversarial loss, employing generative adversarial networks (GANs) and introducing image translation techniques that do not require pairwise data. Methods that leverage multi-style models are capable of transforming images into various styles without necessitating model retraining. These modelling techniques are versatile, and require less time to train models for new styles.