Adaptive Attention Module for Image Recognition Systems in Autonomous Driving

Lightweight, high-performance networks are important in vision perception systems. Recent research on convolutional neural networks has shown that attention mechanisms can significantly improve the network performance. However, existing approaches either ignore the significance of using both types of attention mechanisms (channel and space) simultaneously or increase the model complexity. In this study, we propose the adaptive attention module (AAM), which is a truly lightweight yet effective module that comprises channel and spatial submodules to balance model performance and complexity. The AAM initially utilizes the channel submodule to generate intermediate channel-refined features. In this module, an adaptive mechanism enables the model to autonomously learn the weights between features extracted by global max pooling and global average pooling to adapt to different stages of the model, thus enhancing performance. The spatial submodule employs a group-interact-aggregate strategy to enhance the expression of important features. It groups the intermediate channel-refined features along the channel dimension into multiple subfeatures for parallel processing and generates spatial attention feature descriptors and channelwise refined subfeatures for each subfeature; subsequently, it aggregates all the refined subfeatures and employs a “channel shuffle” operator to transfer information between different subfeatures, thereby generating the final refined features and adaptively emphasizing important regions. Additionally, AAM is a plug-and-play architectural unit that can be directly used to replace standard convolutions in various convolutional neural networks. Extensive tests on CIFAR-100, ImageNet-1k, BDD100K, and MS COCO demonstrate that AAM improves the baseline network performance under various models and tasks, thereby validating its versatility.