CBAM: Convolutional Block Attention Module

Abstract

We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. Because CBAM is a lightweight and general module, it can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs. We validate our CBAM through extensive experiments on ImageNet-1K, MS COCO detection, and VOC 2007 detection datasets. Our experiments show consistent improvements in classification and detection performances with various models, demonstrating the wide applicability of CBAM. The code and models will be publicly available.

Keywords

Computer scienceFeature (linguistics)Block (permutation group theory)Convolutional neural networkPattern recognition (psychology)Code (set theory)Artificial intelligenceAlgorithmMathematicsProgramming language

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Publication Info

Year: 2018
Type: book-chapter
Pages: 3-19
Citations: 20102
Access: Closed

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APA Style

                            
                                    Sanghyun Woo, 
                                
                                    Jongchan Park, 
                                
                                    Joon‐Young Lee
                                
                                et al.
                            
                            (2018). 
                            CBAM: Convolutional Block Attention Module. 
                            Lecture notes in computer science
                            
                            , 3-19.
                            https://doi.org/10.1007/978-3-030-01234-2_1

Identifiers

DOI: 10.1007/978-3-030-01234-2_1
PMID: 41292690
PMCID: PMC12640887
arXiv: 1807.06521

Data Quality

Data completeness: 79%