CBAM Paper Walkthrough: The Double-Attention Mechanism
As the name suggests, this is essentially a block we can attach to a CNN-based model to enhance feature quality by performing an attention mechanism. Despite the name attention, it is completely different from the one in the ViT (Vision Transformer) architecture. Keep in mind that CBAM was first released in 2018, while ViT was only introduced in 2020.
- ▪As the name suggests, this is essentially a block we can attach to a CNN-based model to enhance feature quality by performing an attention mechanism.
- ▪Despite the name attention, it is completely different from the one in the ViT (Vision Transformer) architecture.
- ▪Keep in mind that CBAM was first released in 2018, while ViT was only introduced in 2020.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/cbam-paper-walkthrough-the-double-attention-mechanism/ |
| Publication time | Sun, 20 Sep 2026 13:00:01 GMT |
| Retrieval time | 2026-09-20T13:13:46.673Z |
| Last seen | 2026-09-20T13:13:46.673Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Deep LearningCBAM Paper Walkthrough: The Double-Attention MechanismUnderstanding and implementing CBAM (Convolutional Block Attention Module) from scratch with PyTorchMuhammad Ardi PutraSeptember 20, 202623 min readPhoto by Waldemar Brandt on UnsplashIntroductionIn this article, I am going to review and implement the deep learning paper titled “CBAM: Convolutional Block Attention Module” by Woo et al. [1]. As the name suggests, this is essentially a block we can attach to a CNN-based model to enhance feature quality by performing an attention mechanism. Despite the name attention, it is completely different from the one in the ViT (Vision Transformer) architecture. Keep in mind that CBAM was first released in 2018, while ViT was only introduced in 2020.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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