Attention Mechanism is a technique used in deep learning models that allows the network to focus on specific parts of input data while processing it. In natural language processing, attention mechanisms enable the model to selectively attend to certain words or phrases in a sentence when generating a response.
In computer vision, attention mechanisms allow the model to selectively focus on specific regions of an image when performing tasks like object detection or image captioning. The mechanism works by assigning weights to different parts of the input data, allowing the network to assign more importance to certain parts of the data and less importance to others. This helps improve the accuracy of the model and reduces the number of computations needed to process the input data.
Attention mechanisms have shown impressive results in a variety of deep learning applications and have become an important area of research in the field of artificial intelligence.