  While existing semi-supervised object detection (SSOD) methods perform well
in general scenes, they encounter challenges in handling oriented objects in
aerial images. We experimentally find three gaps between general and oriented
object detection in semi-supervised learning: 1) Sampling inconsistency: the
common center sampling is not suitable for oriented objects with larger aspect
ratios when selecting positive labels from labeled data. 2) Assignment
inconsistency: balancing the precision and localization quality of oriented
pseudo-boxes poses greater challenges which introduces more noise when
selecting positive labels from unlabeled data. 3) Confidence inconsistency:
there exists more mismatch between the predicted classification and
localization qualities when considering oriented objects, affecting the
selection of pseudo-labels. Therefore, we propose a Multi-clue Consistency
Learning (MCL) framework to bridge gaps between general and oriented objects in
semi-supervised detection. Specifically, considering various shapes of rotated
objects, the Gaussian Center Assignment is specially designed to select the
pixel-level positive labels from labeled data. We then introduce the
Scale-aware Label Assignment to select pixel-level pseudo-labels instead of
unreliable pseudo-boxes, which is a divide-and-rule strategy suited for objects
with various scales. The Consistent Confidence Soft Label is adopted to further
boost the detector by maintaining the alignment of the predicted results.
Comprehensive experiments on DOTA-v1.5 and DOTA-v1.0 benchmarks demonstrate
that our proposed MCL can achieve state-of-the-art performance in the
semi-supervised oriented object detection task.
