👉 CAMs, or Convolutional Attention Mechanisms, are a type of neural network architecture designed to enhance the performance of Convolutional Neural Networks (CNNs) in tasks involving sequential data, such as video analysis and time-series prediction. CAMs integrate attention mechanisms with convolutional layers, allowing the network to focus on specific spatial regions within an input image that are most relevant for making predictions. This is achieved by computing attention maps at each spatial location, which highlight the importance of different parts of the image. By dynamically weighting these regions, CAMs improve the model's ability to capture long-range dependencies and contextual information, leading to better performance in tasks that require understanding the relationships between different parts of the input data.
