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.

提出了域对抗 (Domain Contrast, DC)，一种受对比学习启发的简单而有效的方法，可用于训练域自适应检测器。DC 通过交叉域对比损失实现，保证了检测器的可转移性，并自然地缓解了目标域中的类别不平衡问题。广泛实验表明，DC 显著提高了基线和最先进技术，同时展示了大域分歧的巨大潜力。