Dynamic Supervisor for Cross-dataset Object Detection
This addresses a practical problem in computer vision for researchers and practitioners working with multiple object detection datasets, though it appears incremental as it builds on existing annotation generation approaches.
The paper tackles the problem of cross-dataset object detection where inconsistent category ranges transform fully supervised into semi-supervised learning, proposing a dynamic supervisor framework that updates annotations multiple times using hard and soft labels to significantly improve both recall and precision.
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.