Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature Integration

Understand

In recent years, attention mechanisms have significantly enhanced the performance of object detection by focusing on key feature information.

• However, prevalent methods still encounter difficulties in effectively balancing local and global features.
• This imbalance hampers their ability to capture both fine-grained details and broader contextual information-two critical elements for achieving accurate object detection.To address these challenges, we propose a novel attention mechanism, termed Local-Global Attention, which is designed to better integrate both local and global contextual features.
• Specifically, our approach combines multi-scale convolutions with positional encoding, enabling the model to focus on local details while concurrently considering the broader global context.