Adaptive Dense Pseudo Label Selection for Semi-supervised Oriented
  Object Detection
Tong Zhao Qiang Fang Shuohao Shi Xin Xu
Recently, dense pseudo-label, which directly selects pseudo labels from the
original output of the teacher model without any complicated post-processing
steps, has received considerable attention in semi-supervised object detection
(SSOD). However, for the multi-oriented and dense objects that are common in
aerial scenes, existing dense pseudo-label selection methods are inefficient
and impede the performance in semi-supervised oriented object detection.
Therefore, we propose Adaptive Dense Pseudo Label Selection (ADPLS) for
semi-supervised oriented object detection. In ADPLS, we design a simple but
effective adaptive mechanism to guide the selection of dense pseudo labels.
Specifically, we propose the mean Feature-Richness Score (mFRS) to estimate the
density of potential objects and use this score to adjust the number of dense
pseudo labels. On the DOTA-v1.5 benchmark, the proposed method outperforms
previous methods especially when labeled data are scarce. For example, it
achieves 49.78 mAP given only 5% of annotated data, which surpasses previous
state-of-the-art method given 10% of annotated data by 1.15 mAP. Our codes will
be available soon.