Average Precision Loss

Average Precision (AP) loss functions aim to directly optimize object detection models for the AP metric, a crucial measure of detection accuracy encompassing both localization and classification performance. Current research focuses on improving AP loss formulations by addressing issues like misalignment between classification confidence and localization precision, and developing more effective strategies for selecting relevant training pairs. This work leverages various model architectures, including DETR and its variants, and explores techniques like adaptive pairwise error calculations and parameterized AP loss functions. Ultimately, these efforts seek to enhance the training efficiency and accuracy of object detectors, leading to improved performance in real-world applications.