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In this paper, we introduce Fast&Focused-Net, a novel deep neural networkarchitecture tailored for efficiently encoding small objects into fixed-lengthfeature vectors. Contrary to conventional Convolutional Neural Networks (CNNs),Fast&Focused-Net employs a series of our newly proposed layer, the Volume-wiseDot Product (VDP) layer, designed to address several inherent limitations ofCNNs. Specifically, CNNs often exhibit a smaller effective receptive field thantheir theoretical counterparts, limiting their vision span. Additionally, theinitial layers in CNNs produce low-dimensional feature vectors, presenting abottleneck for subsequent learning. Lastly, the computational overhead of CNNs,particularly in capturing diverse image regions by parameter sharing, issignificantly high. The VDP layer, at the heart of Fast&Focused-Net, aims toremedy these issues by efficiently covering the entire image patch informationwith reduced computational demand. Experimental results demonstrate the prowessof Fast&Focused-Net in a variety of applications. For small objectclassification tasks, our network outperformed state-of-the-art methods ondatasets such as CIFAR-10, CIFAR-100, STL-10, SVHN-Cropped, and Fashion-MNIST.In the context of larger image classification, when combined with a transformerencoder (ViT), Fast&Focused-Net produced competitive results for OpenImages V6,ImageNet-1K, and Places365 datasets. Moreover, the same combination showcasedunparalleled performance in text recognition tasks across SVT, IC15, SVTP, andHOST datasets. This paper presents the architecture, the underlying motivation,and extensive empirical evidence suggesting that Fast&Focused-Net is apromising direction for efficient and focused deep learning.

Enhancing Small Object Encoding in Deep Neural Networks: Introducing Fast&Focused-Net with Volume-wise Dot Product Layer