Fine Grained Object Detection

Fine-grained object detection (FGOD) aims to accurately identify and classify objects within a category that share similar visual characteristics, a challenge exceeding the capabilities of standard object detection. Current research focuses on improving model robustness to noisy annotations (e.g., from crowdsourcing), addressing class imbalance issues (long-tailed distributions), and enhancing feature representation to better distinguish subtle differences between similar objects, often employing techniques like orthogonal mapping and Bayesian approaches for improved accuracy and efficiency. These advancements have significant implications for various applications, including ecological monitoring, remote sensing, and medical image analysis, where precise identification of visually similar objects is crucial.