Object Detection Baseline

Object detection baselines serve as foundational models for evaluating advancements in object detection algorithms. Current research focuses on improving baseline performance through techniques like spatio-temporal fusion for video data, guided posterior regularization for challenging datasets (e.g., medical images with overlapping objects), and energy-efficient architectures for resource-constrained devices. These improvements are crucial for advancing applications ranging from autonomous driving and medical image analysis to efficient edge computing, where robust and resource-conscious object detection is paramount.