Attention mechanisms are techniques used in machine learning, particularly in natural language processing and computer vision, to improve the performance of models. They allow models to focus on specific parts of the input data that are most relevant for making predictions, rather than treating all input equally. This selective focus helps capture important relationships and context, enhancing the model's understanding. In the context of neural networks, attention mechanisms can be implemented in various architectures, such as Transformers. These models use attention scores to weigh the importance of different input elements, enabling them to generate more accurate outputs. This approach has significantly advanced tasks like language translation and image captioning.