Masked and Adaptive Transformer for Exemplar Based Image Translation (2303.17123v1)

Abstract: We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at \url{https://github.com/AiArt-HDU/MATEBIT}.

• The paper introduces a Masked and Adaptive Transformer (MAT) that refines cross-domain semantic correspondence for exemplar-based image translation.
• MAT employs a masked attention mechanism and adaptive convolution blocks to filter noise and enhance contextual feature augmentation.
• Results demonstrate that the method outperforms state-of-the-art techniques in semantic consistency, style fidelity, and perceptual image quality.

Analyzing the Masked and Adaptive Transformer for Exemplar Based Image Translation

The paper "Masked and Adaptive Transformer for Exemplar Based Image Translation" introduces a novel approach to improve the accuracy and quality of exemplar-based image translation. The core innovation lies in the integration of a Masked and Adaptive Transformer (MAT) which enhances the process of cross-domain correspondence learning while diminishing the errors typically associated with semantic matching across different domains.

Overview of the Proposed Method

Exemplar-based image translation often relies on establishing cross-domain semantic correspondence to facilitate local style control. This conventional method encounters significant challenges due to the complexity and potential inaccuracies in semantic matching. The authors address these challenges by proposing MAT, which improves the reliability of semantic correspondence and integrates additional contextual understanding through feature augmentation.

MAT utilizes a masked attention mechanism that distinguishes between reliable and unreliable correspondences, effectively filtering out noise and focusing on accurate semantic matches. This approach, combined with adaptive convolution blocks for context-aware feature augmentation, makes the MAT capable of producing images with superior semantic consistency and style fidelity.

Moreover, by leveraging both local and global style information, the method achieves a balance that enhances overall image quality. The use of contrastive style learning (CSL) further refines style representation by employing negative examples generated during early training phases, emphasizing subtle distinctions in quality as well as style.

Key Results and Contributions

The experimental results demonstrate that the MAT framework, referred to as MATEBIT in the paper, consistently outperforms existing state-of-the-art methods in various tasks, such as semantic consistency, style fidelity, and overall perceptual quality. Across multiple datasets, including CelebA-HQ, Metfaces, and DeepFashion, the method achieved lower Fréchet Inception Distance (FID) and Sliced Wasserstein Distance (SWD) scores, indicative of higher generated image quality.

The reformulation of the correspondence and decoding strategies through MAT addresses the limitations of previous methods that heavily depended on correspondence accuracy. The integration of source features from input images with global style codes from exemplars provides a robust framework that improves the visual realism of the translated images.

Implications and Future Directions

The implications of this research are significant for multiple fields, especially where high-fidelity image generation and translation are crucial. The MAT framework, by reducing the errors associated with semantic matching, opens new avenues in computer vision applications, including photography, virtual reality, and the broader domain of digital art.

Looking forward, the methodology sets a foundation for further exploration in improving style transfer techniques. Future work could explore enhancing domain adaptation techniques, improving semantic parsing accuracy, or integrating MAT within more complex transformation networks to push the boundaries of what can be achieved with exemplar-based image translation.

In conclusion, the authors have made a substantial contribution to the field of image translation by introducing a framework that circumvents traditional limitations and sets a new benchmark for quality and efficiency in exemplar-based methods. The synergistic combination of advanced transformer dynamics, contextual feature adaptation, and contrastive style learning ensures that MATEBIT holds promise for diverse real-world applications.