dcbam

dcbam is a term used in deep learning to denote variants of the Convolutional Block Attention Module that aim to improve feature refinement in convolutional neural networks by adding dynamic or expanded attention capabilities. The label is not tied to a single canonical architecture; rather, it covers several implementations that share the goal of enhancing where and how attention is applied to feature maps.

Background: The Convolutional Block Attention Module (CBAM) proposed by Woo and colleagues applies channel attention and spatial attention in sequence to refine feature maps with minimal overhead. dcbam variants extend this idea with adaptations intended to increase flexibility or receptive field.

Variants and design choices include dynamic attention branches conditioned on the input, dilated or multi-scale attention to capture longer-range dependencies, and integration strategies with residual or dense blocks to preserve efficiency. Some approaches combine both channel and spatial attention with input-dependent parameters.

Applications: dcbam ideas have been explored in image classification, object detection, segmentation, and video processing, offering potential accuracy gains with relatively small increases in computation compared with heavier attention modules.

Evaluation and considerations: reported gains depend on task and architecture; benefits may be modest in some baselines. Practical concerns include extra hyperparameters, training stability, and the need to balance performance with inference time.

See also: Convolutional Block Attention Module (CBAM); SENet; BAM; attention mechanism; neural networks.