# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 2 Related Works
### Few-Shot Object Counting  
Few-shot object counting (FSC) has emerged as a prominent topic in computer vision in recent years. The core challenge of this research lies in achieving precise counting of annotated objects using datasets with only a limited number of labeled samples. Early research on few-shot learning primarily focused on classification tasks, such as image and text classification [20; 21], while fewer studies have addressed detection and counting tasks [22; 23]. However, since the publication of the first few-shot object detection paper by [PERSON] et al. [24], this area has garnered increasing attention. Many of these studies adopt a dual-branch structure, where one branch processes the support set and the other processes the query set. Object detection under few-shot conditions is achieved using methods such as matching networks.  
In the realm of object counting, traditional methods, such as detection-based [25], density map regression-based [26], and direct regression-based [27] approaches have achieved significant progress. However, their performance tends to deteriorate under few-shot conditions. This is because these traditional methods often rely on a large number of labeled samples for training, and when the number of samples is limited, they are unable to learn comprehensive and accurate feature representations, leading to a decline in counting accuracy. Consequently, researchers have begun exploring the application of few-shot learning to object counting tasks. For instance, [PERSON] et al. [28] proposed a few-shot counting network, FamNet, which can accurately count new categories of objects with only a limited number of labeled samples. This network integrates modules such as feature extraction, ROI pooling, feature correlation layers, and density prediction to achieve precise object density estimation. With advancements in deep learning and reinforcement learning, FSC methods have continued to evolve. Comparing support images and query images in the feature space for similarity matching is an effective strategy. For example, [PERSON] et al. [29] introduced a motivation-based model that incorporates a similarity comparison and feature enhancement modules, which are tightly coupled through a motivation mechanism. This approach successfully addresses the challenges of extracting complex features and matching similarities in FSC, clearly delineates object boundaries, and significantly improves counting accuracy. In addition, [PERSON] et al. [30] proposed a few-shot fruit-counting network by utilizing the Vision Transformer (ViT) and multi-scale features. With its self-attention mechanism, the Vision Transformer can effectively model the spatial relationships among different regions of an object, thus obtaining more discriminative features. This approach focuses on handling multi-scale features in the specific task of fruit counting and has demonstrated excellent counting performance. However, the above-mentioned method neglects to consider the impact of object angle variations on object feature extraction.  
In the context of these existing limitations, this paper adopts a similarity-matching framework as the foundational structure of the model. By integrating novel techniques and methods, the similarity-matching process is refined to enhance the model's ability to capture object feature similarities. This approach not only alleviates challenges associated with limited sample sizes but also takes into account the issue of object angle variations, which is often overlooked in previous studies. By doing so, this paper aims to further improve the accuracy of object counting and provide a more comprehensive solution for object-counting tasks in complex scenarios.