# Dynamic Supervisor for Cross-dataset Object Detection Source: [Dynamic Supervisor for Cross-dataset Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:06:48.603Z Authors: Ze Chen, Zhihang Fu, Jianqiang Huang, Mingyuan Tao, Shengyu Li, Rongxin Jiang, Xiang Tian, Yaowu Chen, Xian-sheng Hua Published: 2022-04-01T03:18:46.000Z ## Abstract The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning. To address this problem, recent studies focus on the generation of high-quality missing annotations. In this study, we first point out that it is not enough to generate high-quality annotations using a single model, which only looks once for annotations. Through detailed experimental analyses, we further conclude that hard-label training is conducive to generating high-recall annotations, while soft-label training tends to obtain high-precision annotations. Inspired by the aspects mentioned above, we propose a dynamic supervisor framework that updates the annotations multiple times through multiple-updated submodels trained using hard and soft labels. In the final generated annotations, both recall and precision improve significantly through the integration of hard-label training with soft-label training. Extensive experiments conducted on various dataset combination settings support our analyses and demonstrate the superior performance of the proposed dynamic supervisor. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()