  This paper focuses on the scale imbalance problem of semi-supervised object
detection(SSOD) in aerial images. Compared to natural images, objects in aerial
images show smaller sizes and larger quantities per image, increasing the
difficulty of manual annotation. Meanwhile, the advanced SSOD technique can
train superior detectors by leveraging limited labeled data and massive
unlabeled data, saving annotation costs. However, as an understudied task in
aerial images, SSOD suffers from a drastic performance drop when facing a large
proportion of small objects. By analyzing the predictions between small and
large objects, we identify three imbalance issues caused by the scale bias,
i.e., pseudo-label imbalance, label assignment imbalance, and negative learning
imbalance. To tackle these issues, we propose a novel Scale-discriminative
Semi-Supervised Object Detection (S^3OD) learning pipeline for aerial images.
In our S^3OD, three key components, Size-aware Adaptive Thresholding (SAT),
Size-rebalanced Label Assignment (SLA), and Teacher-guided Negative Learning
(TNL), are proposed to warrant scale unbiased learning. Specifically, SAT
adaptively selects appropriate thresholds to filter pseudo-labels for objects
at different scales. SLA balances positive samples of objects at different
scales through resampling and reweighting. TNL alleviates the imbalance in
negative samples by leveraging information generated by a teacher model.
Extensive experiments conducted on the DOTA-v1.5 benchmark demonstrate the
superiority of our proposed methods over state-of-the-art competitors. Codes
will be released soon.
