CAM: compound attention module for improved vision models

In recent years, the attention mechanism has played a significant role in enhancing algorithm performance in deep learning-based visual tasks. Most methods focus on developing more complex attention mechanisms to improve network performance, which inevitably increases the computational complexity of the model. To balance performance and computational complexity, this paper proposes the CAM (Compound Attention Module) attention mechanism, which delivers substantial performance improvements with only a slight increase in parameters. The CAM module operates in two dimensions: space and channel. It is a lightweight, plug-and-play module with minimal computational overhead. We validate our CAM module through extensive experiments on image classification and object detection tasks using the CIFAR-100 and VisDrone2019 datasets. Experimental results demonstrate that the model consistently improves image classification and object detection performance, outperforming similar modules.