CBAM: Convolutional Block Attention Module (1807.06521v2)

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.

• The paper introduces a novel CBAM that sequentially applies channel and spatial attention to improve CNN feature representations.
• It details the Channel Attention Module using dual pooling and a shared MLP to capture complementary inter-channel information.
• Experimental results demonstrate significant accuracy gains in ImageNet classification and mAP improvements in object detection tasks.

Convolutional Block Attention Module (CBAM): Enhancing CNN Representation Power

The paper "Convolutional Block Attention Module (CBAM)" introduces a novel attention mechanism designed to augment the performance of Convolutional Neural Networks (CNNs). The primary goal of CBAM is to refine intermediate feature maps through attention-based mechanisms focusing on channel and spatial dimensions independently, which are then sequentially combined to enhance the network's representation ability.

Concept and Design

CBAM is structured into two sub-modules: Channel Attention Module (CAM) and Spatial Attention Module (SAM). The CAM exploits both average-pooled and max-pooled features to capture the inter-channel information, while the SAM leverages a similar strategy to ascertain spatial regions of interest by pooling along the channel axis. The integration sequence of these sub-modules follows a channel-first order which has proven to be more effective.

Channel Attention Module (CAM)

The channel attention mechanism in CBAM builds on the intuition that average and max pooling can reveal different yet complementary aspects of the information in each feature map. By using these two pooling operations, followed by a shared Multi-Layer Perceptron (MLP), CBAM generates a channel-wise attention map that determines the importance of each channel.

Spatial Attention Module (SAM)

For spatial attention, CBAM uses average and max pooling across the channel dimensions to produce 2D maps, which are then concatenated and convolved. This approach focuses on highlighting spatial dependencies, indicating ‘where’ an informative part is located on the feature map.

Experimental Results

Extensive experiments were conducted to validate the efficacy of CBAM across several benchmark datasets and various CNN architectures, including Residual Networks (ResNets), Wide ResNets, and ResNeXts.

ImageNet Classification

CBAM demonstrated significant improvement in classification tasks on the ImageNet-1K dataset. For instance, when applied to ResNet-50, CBAM achieved a top-1 accuracy improvement from 24.56% to 22.66%, compared to baseline and SE-Net improvements. Similarly consistent performance gains were observed across other ResNet variants and architectures.

Object Detection

The benefits of CBAM were also substantiated in the domain of object detection. Utilized in conjunction with Faster R-CNN and applied to MS COCO and VOC 2007 datasets, CBAM-enhanced networks outperformed the baseline models. For instance, CBAM integration with ResNet-101 in Faster R-CNN yielded a mAP@[.5, .95] improvement from 29.1 to 30.8 on the MS COCO dataset.

Visualization and Interpretability

Visualizations using Grad-CAM reinforced the assertions about CBAM's utility. CBAM-integrated networks consistently focused on the salient regions of objects, thereby confirming the effectiveness of the attention module in enhancing the feature extraction process.

Implications and Future Directions

The implications of CBAM are far-reaching. By modularizing attention mechanisms into network architectures, CBAM provides a lightweight yet powerful means of improving the effectiveness of CNNs in several vision tasks. Future research could explore several directions:

• Hybrid Attention Mechanisms: Integrating CBAM with transformer-based models to explore synergies between CNNs and transformers.
• Optimization for Edge Devices: Fine-tuning CBAM for better performance in low-power scenarios, crucial for mobile and embedded applications.
• Multi-Modal Applications: Extending CBAM's principles to multi-modal networks, such as those combining vision and LLMs.

In conclusion, CBAM presents a robust methodology for enhancing the attention capabilities of CNNs, yielding improved performance in various computer vision tasks without significant computational overhead. This advancement significantly contributes to the ongoing development and optimization of neural network architectures.