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1. Exploring Implicit Domain-Invariant
Features for Domain Adaptive Object
Detection
Abstract
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.

2. 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.