Improving Arbitrary Style Transfer with Transformer-based Style Consistency and Contrastive Learning

Core Concepts

The proposed method utilizes a novel Style Consistency Instance Normalization (SCIN) to align content and style features, and an Instance-based Contrastive Learning (ICL) approach to enhance the quality of stylized images by learning stylization-to-stylization relations.

Abstract

The paper introduces an innovative technique to improve the quality of stylized images in arbitrary style transfer. The key contributions are: Style Consistency Instance Normalization (SCIN): This method uses a transformer as a global style extractor to capture long-range and non-local style correlations, and aligns the content features with the style features to provide global style information. Instance-based Contrastive Learning (ICL): This approach learns the stylization-to-stylization relations, which complements the content loss and style loss that only consider the relationship between the stylized image and the content/style image separately. This helps remove artifacts and enhance the quality of the stylized images. Perception Encoder (PE): The authors analyze the limitations of using a fixed VGG network as the feature extractor, which is trained for classification tasks and not well-suited for capturing style features. The proposed PE is designed to extract style information more effectively. Extensive experiments demonstrate that the proposed method generates high-quality stylized images and effectively prevents artifacts compared to existing state-of-the-art methods.

Stats

The paper does not provide any specific numerical data or statistics to support the key claims. The evaluation is primarily based on qualitative comparisons of stylized images and various evaluation metrics such as content fidelity (CF), global effects (GE), local patterns (LP), deception score, and preference score.

Quotes

"Existing arbitrary style transfer methods can be divided into two categories: (a) attention-based style transfer methods. (b) non-attention-based style transfer methods." "To solve these problems, we propose Style Consistency Instance Normalization (SCIN) to align content features with style features from feature distribution, which helps to supply global style information." "Considering existing methods always generate low-quality stylized images with artifacts or stylized images with semantic errors, we introduce a novel Instance-based Contrastive Learning (ICL) to learn stylization-to-stylization relation and remove artifacts."

Key Insights Distilled From

Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning

by Zhanjie Zhan... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13584.pdf

Deeper Inquiries

How can the proposed method be extended to handle video style transfer tasks

The proposed method can be extended to handle video style transfer tasks by incorporating temporal information into the model. One approach could be to use recurrent neural networks (RNNs) or long short-term memory (LSTM) networks to capture the temporal dependencies in video sequences. By feeding consecutive frames of a video into the model, it can learn to transfer the style consistently across frames, creating a stylized video. Additionally, techniques like optical flow estimation can be used to align the style features between frames, ensuring smooth transitions in the stylized video. By adapting the proposed method to handle video data, it can generate high-quality stylized videos with consistent style transfer throughout the sequence.

What are the potential limitations of the Instance-based Contrastive Learning approach, and how can it be further improved

One potential limitation of the Instance-based Contrastive Learning approach is the scalability and efficiency when dealing with a large number of style images. As the number of style images increases, the computation and memory requirements for calculating the contrastive loss also increase, which can become computationally expensive. To address this limitation, techniques like online contrastive learning or memory-efficient contrastive learning can be explored to reduce the computational overhead. Additionally, incorporating techniques like data augmentation or sampling strategies can help in training the model more efficiently with a large number of style images. By optimizing the scalability and efficiency of the Instance-based Contrastive Learning approach, it can be further improved to handle a wide range of style transfer tasks effectively.

Can the Perception Encoder be adapted to other computer vision tasks beyond style transfer, such as image classification or object detection

The Perception Encoder can be adapted to other computer vision tasks beyond style transfer, such as image classification or object detection, by leveraging its ability to capture global style information. In the context of image classification, the Perception Encoder can be used to extract style features from images, which can then be fused with traditional features extracted by convolutional neural networks (CNNs). This fusion of style features can potentially enhance the discriminative power of the model and improve classification accuracy. Similarly, in object detection tasks, the Perception Encoder can be used to extract style information from object regions, aiding in better localization and recognition of objects in images. By integrating the Perception Encoder into these tasks, it can contribute to improving the performance and robustness of computer vision models across various applications.