# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 5 Experiments
### Comparative Experiments  
First, ODConv modules were integrated into ResNet18's Res2 and Res3 stages (individually and jointly) to analyze their impact. Res1 was unchanged to preserve low-level feature extraction, and Res4 remained unmodified for stability. The experiments on SubCATHR-DET (Table 1) showed that replacing both Res2 and Res3 yielded the best performance (mAP: 76.3%). The experimental results demonstrate that replacing the conventional convolutional layers with ODConv modules in both the Res2 and Res3 stages of ResNet18 (denoted as Res2+Res3) achieved optimal model performance, as quantified in Table 1. This enhancement originates from ODConv's strengthened capability to capture fine-grained feature representations during intermediate hierarchical processing. For BRA module evaluation, systematic configurations were implemented by integrating BRA modules after Res2, Res3, or both stages within the ResNet18 backbone. To safeguard low-level feature extraction integrity, the BRA modules were excluded from the initial residual stage, while their omission in the final stage mitigated computational redundancy given diminishing performance returns. Comparative analyses (Table 2) confirmed that dual BRA module deployment (Res2+Res3) yielded peak performance, aligning with theoretical expectations of hierarchical feature refinement. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Precision (\%)** & **mAP (\%)** \\ \hline AugNet & 71.6 & 70.1 \\ ODRes2 & 76.3 & 71.2 \\ ODRes3 & 77.5 & 75.0 \\ ODRes2+ODRes3 & 79.6 & 76.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance with ODConv modules. Building upon the experimental findings, this study developed the AugODNet_BRA algorithm by replacing convolutional layers in the Res2 and Res3 of the ResNet18 backbone with ODConv modules and then integrating BRA modules post these stages. The proposed algorithm, coupled with a multi-granularity retrieval framework, was applied to carrier-based aircraft tail hook recognition tasks for performance evaluation. Comparative validation against the baseline model AugNet and other methods (SPQ, HiHPQ, CIBHash, and MeCoQ) on the SubCATHR-DET dataset demonstrated superior retrieval performance, the results of which are detailed in Table 3.  
As shown in Table 3, the proposed method surpassed all compared methods across metrics, attaining 82.3% precision, 79.1% recall, 80.7% F1 score, and 78.7% mAP. This represents an 8.6% mAP improvement over the baseline method AugNet, confirming the effectiveness of multi-granularity strategies and feature extraction network optimizations. The performance enhancement stems from the Bi-Level Routing Attention mechanism and ODConv modules, which collectively enhanced the fine-grained feature representation capabilities. Concurrently, the integration of coarse-to-fine retrieval strategies improved both local feature discrimination and global structural perception, demonstrating particular efficacy in small object recognition tasks like carrier-based aircraft tail hook recognition (where multi-scale feature preservation is critical). To gain a deeper understanding of the model's performance in retrieval tasks, this paper conducted a meticulous error type analysis of the retrieval errors. The results indicate that the main error types include background interference misjudgment (e.g., where the carrier-based aircraft tail hook's texture and color are highly similar to complex backgrounds, such as the aircraft carrier deck and other parts of the carrier-based aircraft, causing the model to misidentify background elements as tail hooks). Additionally, there is the issue of the weakened features of small targets as the feature details of the tail hook are severely lost in images captured at low resolution or from a distance, making it difficult for the model to effectively capture its key features.