Abstract
Aiming to address the shortcomings of current object detection models, including a large number of parameters, the lack of accurate localization of target bounding boxes, and ineffective detection, this paper proposes a lightweight spatial location attention module (SLAM) that achieves adaptive adjustment of the attention weights of the location information in the feature map while greatly improving the feature representation capability of the network by learning the spatial location information in the input feature map. First, the SLAM module obtains the spatial distribution of the input feature map in the horizontal, vertical, and channel directions through the average pooling and maximum pooling operations, then generates the corresponding location attention weights by computing convolution and activation functions, and finally achieves the weighted feature map by aggregating the features along the three spatial directions respectively. Extensive experiments show that the SLAM module improves the detection performance of the model on the MS COCO dataset and the PASCAL VOC 2012 dataset with almost no additional computational overhead.
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Acknowledgements
This paper is founded by Supported projects of key R & D programs in Hebei Province (No. 21373802D) and Artificial Intelligence Collaborative Education Project of the Ministry of Education (201801003011).
The GPU server in this paper is jointly funded by Shijiazhuang Wusuo Network Technology Co., LTD and Hebei Rouzun Technology Co., LTD.
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Liu, C., Xu, Y., Zhong, J. (2024). SLAM: A Lightweight Spatial Location Attention Module for Object Detection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_29
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DOI: https://doi.org/10.1007/978-981-99-8082-6_29
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