# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 4 Experiments
### Results and Analysis
#### 4.4.2 Comparative Analysis  
**Comparison with MPSR Model**: While both our model and MPSR incorporate multiscale features, our approach consistently delivered higher AP75 scores across most few-shot settings. The specific improvements over MPSR included 4.4% for 3-shot, 7.0% for 5-shot, and 8.0% for 10-shot on PASCAL VOC, and 0.6% for 10-shot and 3.7% for 30-shot on COCO. These improvements can be attributed to the following innovations:  
1. **MultiScale-DynaAttention**: By incorporating multiscale convolutions into the channel attention mechanism, the model captures a broader range of feature scales, enhancing the accuracy and comprehensiveness of feature extraction. 2. **Hierarchical adaptive-pyramid pooling**: Unlike MPSR's fixed pooling strategy, HAPP dynamically adjusts pooling strategies based on input features, enabling the model to better adapt to objects of varying scales and spatial configurations. This dynamic adjustment allows the pooling process to be more context-sensitive, ensuring that important spatial information is not lost and enhancing the model's ability to recognize objects at different scales. In particular, the pooling size and location are adaptively selected based on the object features in the image, which results in a more robust feature map. The dynamic nature of this pooling strategy helps the model efficiently handle scale variations and object localization in few-shot settings. 3. **Residual connections**: The inclusion of residual connections within MultiScale-DynaAttention enhances feature transmission and mitigates potential feature loss, improving overall detection performance.  
**Comparison with TFA model**: Our model also demonstrates superior performance compared to TFA across all few-shot settings. For instance, on the PASCAL VOC dataset with 10-shot samples, our model achieved an AP75 of 65.8%, representing a 9.8% absolute improvement over TFA. Similarly, on the COCO dataset with 30-shot samples, the model reached an AP75 of 15.7%, achieving a 3.7% improvement over TFA. Key factors contributing to this superior performance include  
1. **Dual-branch detection head with HAPP structure**: The dual-branch detection head, equipped with the HAPP structure, enables the more effective utilization of multiscale features. By integrating average pooling and smaller pooling kernels, the model achieves better scale invariance and improves detection accuracy for few-shot objects. 2. **MultiScale-DynaAttention**: As illustrated in Figure 6, unlike TFA, which lacks an effective mechanism for multiscale feature extraction, our model leverages MultiScale-DynaAttention to focus on critical features more effectively. By integrating multiscale convolutions and dynamic pooling strategies, this module enhances the robustness and accuracy of feature representation, addressing the limitations of TFA in capturing scale variations.  
**Qualitative Analysis**: As illustrated in Figure 7, the proposed model excels in detecting objects across a variety of challenging scenarios, including extreme object scales, occlusions, and complex backgrounds. Notably, it demonstrates significant improvements in correcting misclassifications and reducing missed detections. These enhancements are primarily driven by the MultiScale-DynaAttention module and the HAPP structure, which collectively improve feature extraction and localization accuracy.