# GLClick: Interactive Segmentation Combining Global and Local Features
## 1 Introduction  
The rapid advancement of computer vision technologies has made image segmentation a critical task. Image segmentation has promising applications in various domains, such as medical diagnostics [1], autonomous driving [2], and security surveillance [3]. Traditional image segmentation methods [4, 5, 6, 7] typically rely on handcrafted features and complex post-processing techniques. These methods struggle to manage complex scenes and diverse objects. Recently, advancements in deep learning have revolutionized the field, with convolutional neural networks (CNNs) [8] and Transformer-based models [9] significantly improving both accuracy and efficiency. ResNet50 [10], a foundational CNN architecture, has demonstrated strong performance in image classification and segmentation tasks. Its residual blocks effectively mitigate gradient vanishing and model degradation during deep network training, enabling the learning of more detailed and profound feature representations. Despite these strengths,CNNs struggle to capture global context and model long-range dependencies, which can impair their segmentation performance. Transformer models [11], equipped with self-attention mechanisms, have shown a remarkable ability to capture global dependencies within input sequences. This innovation has been successfully adapted to computer vision, particularly for complex tasks like image segmentation. By framing segmentation as a sequence prediction task, transformers utilize global context and long-range dependencies within images. This shift has improved segmentation accuracy while also enhancing model robustness and generalization. However, Transformer models still struggle to capture local details and fine-grained features compared to CNNs. To address this limitation, [PERSON] et al. [9] employed the simple feature pyramid network (SimpleFPN) architecture proposed by [PERSON] et al. [12], to effectively capture scale variations in vision Transformer (ViT) features [11]. This approach has significantly improved segmentation accuracy, advancing related research and applications. Inspired by the SimpleClick framework [9], we propose GLClick, an interactive segmentation model that integrates both global and local perspectives. As illustrated in Figure 1, we introduce the global-local feature fusion module (GLFM), which merges fine-grained local features from ResNet50 with global features from the Transformer feature pyramid. This fusion preserves the Transformer's ability to capture global information while enhancing the extraction of fine local details. These enhancements provide a robust foundation for subsequent segmentation tasks. Experiments across diverse benchmark datasets demonstrate the superior performance of our method in interactive segmentation tasks. Our approach excels in both accuracy and efficiency, especially when handling images with complex structures and textures. These experimental results validate the effectiveness of our method and provide new insights into applying deep learning to image processing tasks. Our main contributions are the following:  
1. We propose GLClick, an interactive segmentation model that integrates both global and local information. GLClick effectively combines global vision with local perception to significantly enhance the effectiveness of interactive segmentation. Figure 1: Comparison between our model and the baseline (SimpleClick) [9]. The green clicks represent foreground clicks, while the red clicks indicate background clicks. 2. We design a global-local feature fusion module (GLFM) to merge feature maps from each layer of ResNet50 with those from the Transformer feature pyramid, contributing to superior results in interactive segmentation tasks. 3. We refine and optimize the segmentation head's multi-layer perceptron (MLP) to achieve greater precision and efficiency in interactive segmentation. The fusion of CNNs and transformers in the GLFM enables the model to perform well on both medical and natural image datasets, demonstrating robustness and versatility.