论文标题
域自适应对象检测的域对比度
Domain Contrast for Domain Adaptive Object Detection
论文作者
论文摘要
我们提出了域对比度（DC），这是一种受训练领域自适应检测器的对比学习启发的简单而有效的方法。从转移模型的误差绑定最小化的角度来推导DC，并以插件和播放的跨域对比度损失实现。通过最大程度地减少跨域的对比损失，DC可以保证探测器的可转移性，同时自然减轻了目标域中的类不平衡问题。 DC可以在图像水平或区域水平上应用，从而始终提高检测器的可传递性和可区分性。对常用基准测试的广泛实验表明，DC通过明显的边缘改善了基线和最先进，同时证明了大域差异的巨大潜力。
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.
