Domain Contrast for Domain Adaptive Object Detection
- URL: http://arxiv.org/abs/2006.14863v1
- Date: Fri, 26 Jun 2020 08:45:36 GMT
- Title: Domain Contrast for Domain Adaptive Object Detection
- Authors: Feng Liu, Xiaoxong Zhang, Fang Wan, Xiangyang Ji, Qixiang Ye
- Abstract summary: Domain Contrast (DC) is an approach inspired by contrastive learning for training domain adaptive detectors.
DC can be applied at either image level or region level, consistently improving detectors' transferability and discriminability.
- Score: 79.77438747622135
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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