  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.
