A Survey: Research Progress of Feature Fusion Technology

Feature fusion techniques represent a critical research content in the domain of deep learning, aiming to concatenate feature information from diverse sources or varying levels to generate more comprehensive and accurate representations. This technology is extensively employed in downstream tasks that necessitate rich target representations, such as image classification, semantic segmentation, and object detection. Over recent years, under the impetus of advancements in deep learning technologies, we have witnessed rapid progress in feature fusion techniques and their profound impact on the entire computer vision field. This paper takes an technique evolutionary perspective to comprehensively summarize the innovative contributions of feature fusion technology within four cutting-edge domains: Convolutional Neural Network (CNN), Vision Transformer (ViT), Graph Convolutional Network (GCN) and Neural Architecture Search (NAS). We provide a detailed introduction to the specific implementation process of each technology, and analytically explores the pivotal roles played by the concept of feature fusion in each of these technologies through different viewpoints. Finally, we provides a systematic overview of the mechanisms behind several classical methods and arrange the open-source code links, and we performance evaluation was conducted on several classic methods.