Recent studies have made tremendous progress in neural style transfer (NST) and various methods have been advanced. However, evaluating and improving the stylization quality remain two important open challenges. Committed to these two aspects, in this paper, we first decompose the quality of style transfer into three quantifiable factors, i.e., the content fidelity (CF), global effects (GE) and local patterns (LP). Then, two novel approaches are further presented for exploiting these factors to improve the stylization quality. The first, named cascade style transfer (CST), utilizes the factors to guide the cascade combination of existing NST methods to absorb their merits and avoid their own shortcomings. The second, dubbed multi-objective network (MO-Net), directly optimizes these factors to balance their performance and achieves more harmonious stylized results. Extensive experiments demonstrate the effectiveness and superiority of our proposed factors and methods.

最近的研究在神经风格转移（NST）方面取得了巨大的进步，并且已经开发了各种方法。但是，评估和改进样式化质量仍然是两个重要的开放挑战。致力于这两个方面，在本文中，我们首先将样式传递的质量分解为三个可量化的因素，即内容保真度（CF），全局效果（GE）和局部模式（LP）。然后，进一步提出了两种新颖的方法来利用这些因素来提高样式化质量。第一种称为级联样式转换（CST），它利用这些因素来指导现有NST方法的级联组合，以吸收其优点并避免其自身的缺点。第二个称为多目标网​​络（MO-Net），直接优化这些因素以平衡其性能并获得更和谐的风格化结果。大量的实验证明了我们提出的因素和方法的有效性和优越性。