2020/06/26 by Feng Liu, Xiaoxong Zhang, Liu, Feng +7 · 3 citations
Computer Science · Engineering · Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Remote-Sensing Image Classification #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2006.14863
arxiv created 2020/06/26 · openalex publication_date 2020/06/26 · arxiv updated 2020/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors. DC is deduced from the error bound minimization perspective of a transferred model, and is implemented with cross-domain contrast loss which is plug-and-play. By minimizing cross-domain contrast loss, DC guarantees the transferability of detectors while naturally alleviating the class imbalance issue in the target domain. DC can be applied at either image level or region level, consistently improving detectors' transferability and discriminability. Extensive experiments on commonly used benchmarks show that DC improves the baseline and state-of-the-art by significant margins, while demonstrating great potential for large domain divergence.