attentionguided

Attention-guided refers to neural network methods in which an attention mechanism is explicitly guided or constrained to focus on informative parts of the input to improve task performance. The approach draws on the attention mechanism popularized in natural language processing and computer vision, and it is used to steer computation, interpretability, or both.

In practice, attention distributions (maps) identify relevant regions, tokens, or modalities. Spatial attention focuses on image regions; channel attention highlights feature maps. Cross-modal attention aligns information across modalities such as text and image.

Techniques to guide attention include auxiliary losses that promote alignment between attention maps and supervisory signals, regularization to encourage sparsity or diversity, and external priors such as saliency maps or part-based annotations.

Applications span image captioning, visual question answering, object detection, segmentation, medical imaging, and remote sensing.

Evaluation considers task performance, qualitative analysis of attention maps, and alignment with human gaze or expert annotations, with attention contributing to robustness and interpretability.

Challenges include potential misalignment from weak supervision, increased computational overhead, and the risk of attention collapse if not properly regularized. As research advances, attention-guided approaches aim to improve data efficiency, localization accuracy, and the transparency of model decision-making while maintaining generalization across domains.