S 3 AHI: Source-Free Domain Adaptive Small Object Detection with Slicing Aided Hyper Inference
Haizhou Ding, Gang Yang, Xiaotong Tu, Yue Huang, Xinghao Ding · 2024
The time-consuming and laborious annotation of small objects has resulted in a relative scarcity of datasets specifically designed for small objects. Additionally, variations in data acquisition devices and application scenarios often cause a domain shift between source-trained data and target data. Unsupervised Domain Adaptation (UDA) is extensively applied to alleviate the domain shift between two domains based on the assumption that source data is accessible during the adaptation process. However, source data may be unavailable in some scenarios due to data privacy or data transmission issues. In this paper, we propose a Source-free domain adaptive framework for Small object detection with Slicing Aided Hyper Inference (S3AHI) in the test-time training stage. Without access to source data, Source-Free Domain Adaptation (SFDA) only employs unlabeled target data to adapt a source-trained model to the target domain during the test phase. SAHI provides a generic and effective solution to detect small objects and can be seamlessly integrated into nearly any pipeline. Motivated by contrastive learning, we build instance-level correlation graphs with the semantic features of proposals and learn high-quality pseudolabels. The S3AHI distills target domain knowledge to the sourcetrained model under the mean-teacher framework. Extensive experiments reveal that our approach outperforms existing SFDA and UDA methods significantly.