mlsanityData to 14 Oct 2026

CBAM: Convolutional Block Attention Module

arXiv 1807.06521 ↗First posted 17 Jul 2018cs.CVcited by 2,477

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Cited by 47 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 2,477 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jul 2018.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 398; 2022: 411; 2023: 425; 2024: 435; 2025: 480; 2026: 326.

2,477 papers.

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