attentionaugmented

Attentionaugmented refers to a class of neural network blocks and architectures that augment traditional convolutional neural networks with attention mechanisms to capture both local and long-range dependencies in visual data. The core idea is to combine the strengths of convolution, which excels at modeling local structure, with self-attention, which can model global context.

In a typical attentionaugmented block, two parallel paths run on the same input: a convolutional path that processes local features and an attention path that computes a global context. The attention path usually follows a self-attention formulation in which the input is projected to queries, keys, and values. Attention weights are computed as a softmax over the dot products of queries and keys, and these weights are used to form a context-rich representation from the values. This representation is then projected back to the original channel dimension and fused with the convolutional features, commonly via addition or concatenation and a subsequent projection. The result is a feature map that blends local detail with global information.

Attentionaugmented blocks are designed to improve performance on tasks such as image classification, object detection, and segmentation, while aiming to maintain efficiency relative to full transformer-based models. Variants differ in how attention is applied (spatial vs. channel attention), the number of attention heads, how the two paths are fused, and where in the network the blocks are inserted. They are often used to augment existing CNN backbones, such as ResNet-like architectures, to enhance representational power without a complete architectural overhaul.

Limitations can include increased computational cost and memory usage, potential optimization challenges, and mixed gains depending on the dataset and task. Related concepts include non-local neural networks and other attention mechanisms used within convolutional frameworks.

See also: self-attention, non-local networks, convolutional neural networks, transformer models.