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

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