Dynamic Supervisor for Cross-dataset Object Detection
- URL: http://arxiv.org/abs/2204.00183v1
- Date: Fri, 1 Apr 2022 03:18:46 GMT
- Title: Dynamic Supervisor for Cross-dataset Object Detection
- Authors: Ze Chen, Zhihang Fu, Jianqiang Huang, Mingyuan Tao, Shengyu Li,
Rongxin Jiang, Xiang Tian, Yaowu Chen and Xian-sheng Hua
- Abstract summary: 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.
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.
- Score: 52.95818230087297
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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