The article introduces a new method called Multiactivation Pooling (MAP) in Convolutional Neural Networks (CNNs) for better image recognition. Unlike traditional pooling methods, MAP uses larger pooling regions and top-k activation to improve classification accuracy without adding complexity. By testing on popular models like VGG, ALL-CNN, and DenseNets, the researchers found that the MAP method produced competitive results on various benchmark datasets like CIFAR-10, CIFAR-100, SVHN, and ImageNet.