Long Tailed Object Detection

Long-tailed object detection addresses the challenge of training object detectors on datasets where some classes have far fewer examples than others, a common real-world scenario. Current research focuses on mitigating biases in both classification and regression components of detectors, employing techniques like class-agnostic branches, hierarchical losses, and adaptive sampling methods to improve performance on under-represented classes. These advancements are crucial for improving the robustness and generalizability of object detection models in diverse applications, such as autonomous driving and broader image understanding tasks where rare classes are often critical. The field is actively exploring both single-stage and two-stage detector architectures, along with semi-supervised and multi-modal approaches to leverage limited data effectively.