Exploring Implicit Domain-Invariant Features for Domain Adaptive Object Detection

Recent researches have made a great progress in domain adaptive object detectors. These detectors aim to learn explicit domain-invariant features by adversarially mitigating domain divergence and simultaneously optimizing source risks. However, an inherent problem is that they ignore the informative knowledge implied in domain-specific features, which is recognized as implicit domain-invariant feature. This is mainly caused by the multimode structure underlying target distribution, characterized by various scales and categories of objects in target images. To solve that, we propose the Implicit Domain-invariant Faster R-CNN (IDF) by using non-adversarial domain discriminator, dual attention mechanism and selective feature perception. This idea is implemented on the Faster R-CNN backbone, but with an improved architecture of two branches, i.e. domain-invariant branch and domain-specific branch. The former can clearly learn explicit domain adaptive features w.r.t. easy samples, while the latter aims to learn implicit domain-invariant features w.r.t. hard samples. Experiments on numerous benchmark datasets, including the Cityscapes, Foggy Cityscapes, KITTI and SIM10K, show the superiority of our IDF over other state-of-the-art domain adaptive object detectors. The demo code is released in https://github.com/sea123321/IDF.