## 1 Introduction  
In recent years, deep learning [1] has achieved remarkable success in various computer vision (CV) tasks, such as image classification [2], object detection [3, 4], and semantic segmentation [5]. However, deep learning (DL) models often suffer from heavy computational burdens due to large numbers of parameters and high-dimensional input data, limiting their practical applications [6]. In particular, the proliferation of smart devices and IoT (Internet of Things) sensors has given rise to a pressing need for edge computing [7] as edge computing enables computation near data sources or things. To deploy DL models on resource-limited edge devices, reducing model complexity has become a priority [8, 9]. Various techniques have been proposed for reducing the complexity of DL models, among which downsampling plays a crucial role [10]. However, most existing downsampling methods tend to lose some detailed information [11]. Thus, it remains a challenging problem to design a lightweight and efficient downsampling component which can retain more semantic and detailed information with lower algorithmic complexity. In this article, we address this issue by proposing a novel, high-performance slicing downsampling component. In many computer vision tasks, neural network downsampling is a crucial technique that is used to reduce the spatial resolution of the feature map [11; 12]. It can effectively reduce the computational and memory requirements of the network and expand the receptive field while retaining important information for subsequent processing [13]. It reduces the spatial resolution by proportionally scaling down the width and height of feature maps, which can be achieved by selecting a subset of features or by aggregating the features in local regions. Downsampling can help to regularize the network and prevent overfitting by reducing the number of parameters and introducing some degree of spatial invariance [14]. It can improve the efficiency of the neural network in processing large-scale complex data, such as remote sensing images and videos, and enable the DL models to operate on resource-limited devices [15]. Pooling or subsampling of feature maps, such as Max Pooling or Strided Convolution, is a common downsampling operation in the neural network [16]. However, most of the methods condense regional features to a single output, which suffers from several challenges, such as information loss and spatial bias [11]. For instance, Max Pooling only retains the most distinguishable features [17], and subsampling picks a portion of features randomly or according to rules [18; 19], while the slicing adopted in this work utilizes the full information in the input feature map, as shown in Figure 1. Therefore, the research on neural network downsampling is still an active area where there is space for optimization, and more efficient methods need be developed for better retaining feature information. Figure 1: Feature information retained by different downsampling methods. Upsampling also plays an important role in neural networks. It is often used for image super-resolution [20], segmentation [21], and generation [22] tasks via the reconstruction of high-resolution feature maps during the decoding stage in the neural network [23]. The main upsampling methods include interpolation-based upsampling such as the Nearest Neighbor, Bilinear, and Bicubic Interpolation methods [24] and the Transposed Convolution [25] and Sub-Pixel Convolutional [26] methods. The simplest and fastest algorithm is Nearest Neighbor sampling in which each pixel is copied in four copies to a 2 \(\times\) 2 neighborhood; however, jagged edges are often introduced [27]. The Bilinear and Bicubic Interpolation methods calculate new pixel values via the weighted averaging of the nearest pixels in the original image, providing smoother results than Nearest Neighbor Upsampling yet still introducing some blurring [28]. Transposed Convolution, also known as deconvolution or fractionally strided convolution, is the reverse operation of convolution.