S$^2$Teacher: Step-by-step Teacher for Sparsely Annotated Oriented Object Detection
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
Although fully-supervised oriented object detection has made significant
progress in multimodal remote sensing image understanding, it comes at the cost
of labor-intensive annotation. Recent studies have explored weakly and
semi-supervised learning to alleviate this burden. However, these methods
overlook the difficulties posed by dense annotations in complex remote sensing
scenes. In this paper, we introduce a novel setting called sparsely annotated
oriented object detection (SAOOD), which only labels partial instances, and
propose a solution to address its challenges. Specifically, we focus on two key
issues in the setting: (1) sparse labeling leading to overfitting on limited
foreground representations, and (2) unlabeled objects (false negatives)
confusing feature learning. To this end, we propose the S$^2$Teacher, a novel
method that progressively mines pseudo-labels for unlabeled objects, from easy
to hard, to enhance foreground representations. Additionally, it reweights the
loss of unlabeled objects to mitigate their impact during training. Extensive
experiments demonstrate that S$^2$Teacher not only significantly improves
detector performance across different sparse annotation levels but also
achieves near-fully-supervised performance on the DOTA dataset with only 10%
annotation instances, effectively balancing detection accuracy with annotation
efficiency. The code will be public.
