# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
###### Abstract  
This paper proposes a multi-granularity retrieval algorithm based on an unsupervised image augmentation network. The algorithm designs a feature extraction method (AugODNet_BRA) rooted in image augmentation, which efficiently captures high-level semantic features of images with few samples, small targets, and weak features through unsupervised learning. The Omni-Dimensional Dynamic Convolution module and Bi-Level Routing Attention mechanism are introduced to enhance the model's adaptability to complex scenes and variable features, thereby improving its capability to capture details of small targets. The Omni-Dimensional Dynamic Convolution module flexibly adjusts the dimensions of convolution kernels to accommodate small targets of varying sizes and shapes. At the same time, the Bi-Level Routing Attention mechanism adaptively focuses on key regions, boosting the model's discriminative ability for targets in complex backgrounds. The optimized loss function further enhances the robustness and distinctiveness of features, improving retrieval accuracy. The experimental results demonstrate that the proposed method outperforms baseline algorithms on the public dataset CUB-200-2011 and exhibits great potential for application and practical value in scenarios such as carrier-based aircraft tail hook recognition. multi-granularity; unsupervised image augmentation network; Omni-Dimensional Dynamic Convolution; Bi-Level Routing Attention 2025 27 April 2025 22 May 2025  
Data with few samples and weak features are widely found in many fields, such as medical image analysis, object detection, remote sensing image recognition, and underwater sonar image localization. In these fields, obtaining samples is often difficult or costly, resulting in small-scale datasets that cannot meet the demand for large-scale training data required by traditional machine learning or deep learning methods. Moreover, the weak feature characteristics of the data, such as low resolution, high noise, and complex backgrounds, further increase the difficulty of feature extraction and pattern recognition. Therefore, developing efficient retrieval algorithms that can effectively deal with data with few samples and weak features is of great theoretical and practical significance for advancing technological progress and practical applications in these fields. To address the challenges of retrieving data with few samples and weak features, this paper proposes a multi-granularity retrieval algorithm based on an unsupervised image augmentation network. The algorithm designs a feature extraction method (AugOD-Net_BRA) based on image augmentation, which efficiently captures the high-level semantic features of images with few samples, small targets, and weak features. By introducing the Omni-Dimensional Dynamic Convolution module and the Bi-Level Routing Attention mechanism, the model's adaptability to complex scenes and multi-scale features is enhanced. An optimized loss function further improves the robustness and distinctiveness of features. Experimental results show that the proposed method outperforms baseline methods on the public dataset CUB-200-2011 and the Carrier-based Aircraft Tail Hook Recognition Dataset (SubCATHR-DET), demonstrating good application potential and practical value. The main contributions of this paper are as follows:  
1. A multi-granularity retrieval algorithm for data with few samples and weak features is proposed, effectively addressing the shortcomings of traditional methods in these scenarios. 2. The Omni-Dimensional Dynamic Convolution module and Bi-Level Routing Attention mechanism are introduced, significantly enhancing the model's adaptability to complex scenes and small targets. 3. An optimized loss function was designed to improve the robustness and distinctiveness of features, thereby increasing retrieval accuracy. 4. Experiments on the public dataset CUB-200-2011 and the SubCATHR-DET dataset helped to verify the effectiveness and practicality of the proposed method.