# A Lightweight Module for Feature Fusion Based on Their Dimensions
###### Abstract  
With the popularity of mobile devices, lightweight deep learning models have important value in various application scenarios. However, how to effectively fuse the feature information from different dimensions while ensuring the model's lightness and high accuracy is a problem that has not been fully solved. In this paper, we propose a novel feature fusion module, called _g_unit, which can fuse the features extracted by different dimensional networks according to the order of feature information with a small computational cost, avoiding the problems of information fragmentation caused by simple feature stacking in traditional information fusion. Based on _g_unit, this paper further builds an extremely lightweight model _g_Net, which can achieve performance close to the highest accuracy on several public datasets under the condition of very limited parameter scale. The core idea of _g_unit is to use deconvolution to reduce the discrepancy among the features to be fused, and to lower the possibility of feature information fragmentation after fusion by fusing the features from different dimensions sequentially. _g_Net is a lightweight network composed of multiple _g_units and bottleneck modules, with a parameter scale of only 1.24 M, much smaller than traditional lightweight models. This paper conducts experiments on public datasets, and _g_Net achieves an accuracy of 71.64% on the food101 dataset, and an accuracy of 75.31% on the random 50-category food101 dataset, both higher than or close to the highest accuracy. This paper provides a new idea and method for feature fusion of lightweight models, and also provides an efficient model selection for deep learning applications on mobile devices. image classification; lightweight model; _g_unit; _g_Net  
complexity, and its branch-like topology structure could also ensure that it could learn more spatial correlation features [22], such as GoogleNet [23] and its derived networks SqueezeNet [24] and Xception [25]. However, these networks still have limitations in information extraction ability. To distinguish the importance of different information, researchers introduced channel attention mechanism, such as SE [26], CBAM [27] and ECA [28], which improved model performance by recalibrating feature responses. As CNN gradually encountered bottlenecks in the pursuit of high accuracy, the emergence of ViT [29] aroused researchers' strong interest in transformer. Transformer's completely different network structure and higher accuracy from CNN attracted a large number of researchers to explore it in depth. However, the huge computation and parameter amount of transformer limited its application on mobile devices with limited resources (such as smartphones and unmanned driving devices). Although there are some methods, such as network pruning [30; 31; 32; 33], knowledge distillation [34; 35; 36], structure reparameterization [37], model quantization [38; 39], etc., that can reduce the parameter amount and computation of the model without significantly reducing the accuracy, these methods are limited by the model itself and cannot solve the problem fundamentally. Therefore, considering the demand of deploying and running lightweight models on mobile devices, developing efficient network structures based on CNN to improve the feature information extraction density of CNN modules, and reducing the parameter amount and computation of the network, is promising work [40]. In conventional network architectures, feature information from different network levels is fused by first determining whether convolution is needed to align the feature shapes, and then applying concatenation or addition operation. These operations are commonly used across different feature extraction layers to aggregate features from various levels [41]. However, they merely combine features without taking into account the spatial relationship among different information sources.