Backbone Network

Backbone networks are fundamental building blocks in many deep learning models, particularly in computer vision, serving as feature extractors for downstream tasks like object detection and segmentation. Current research emphasizes improving their efficiency, often by combining convolutional and transformer architectures, and exploring methods for automated design through neural architecture search (NAS) and evolutionary algorithms. This focus on optimization aims to enhance both accuracy and speed across diverse hardware platforms, ultimately impacting the performance and applicability of numerous AI systems in various fields.