Backbone Architecture

Backbone architectures are the foundational feature extraction components of many deep learning models, crucial for tasks like object detection and visual tracking. Current research focuses on optimizing these architectures for efficiency (e.g., minimizing latency and energy consumption on mobile devices), improving accuracy in challenging scenarios (e.g., tiny object detection, multimodal data fusion), and enhancing feature representation learning through techniques like target-aware interactions and domain adaptation. These advancements significantly impact various applications, from autonomous driving and mobile AI to remote sensing and visual tracking, by improving both the speed and accuracy of these systems.