Enhanced Foreground Extraction Using Visual Transformers and Semantic Guidance: A Practical Framework for Adobe Photoshop
Kun Lv · Advances in transdisciplinary engineering · 2025
Computer vision techniques have become integral to multidisciplinary applications such as object segmentation, content-aware editing, and compositing, where accurate foreground extraction is essential. These applications span fields including digital media, graphic design, and artificial intelligence, underscoring the relevance of integrating technological advancements into creative and practical workflows. However, challenges such as cluttered backgrounds, ambiguous boundaries, and fine object details persist in achieving robust and precise extraction results. This paper proposes a novel framework that integrates Visual Transformers with a Semantic-Guided Module to address these limitations. The Visual Transformer utilizes self-attention mechanisms to model global dependencies, enabling effective foreground-background separation in complex scenes. Meanwhile, the Semantic-Guided Module incorporates high-level semantic priors to refine local features and enhance boundary precision, addressing the shortcomings of existing convolutional neural network-based methods. Experimental results on benchmark datasets, including COCO and ADE20K, demonstrate the proposed framework’s superior performance. The model achieves significant improvements in Intersection over Union (IoU), F1 Score, and precision metrics compared to state-of-the-art approaches. Qualitative analyses further confirm its robustness in handling intricate details, such as hair and transparent objects, while maintaining computational efficiency. The integration of this framework into a prototype Adobe Photoshop plugin highlights its interdisciplinary applicability, bridging the gap between cutting-edge computer vision research and professional image editing workflows. This research establishes a strong foundation for advancing segmentation methodologies, addressing real-world challenges, and enhancing image editing workflows across diverse domains.