  Detectors trained with massive labeled data often exhibit dramatic
performance degradation in some particular scenarios with data distribution
gap. To alleviate this problem of domain shift, conventional wisdom typically
concentrates solely on reducing the discrepancy between the source and target
domains via attached domain classifiers, yet ignoring the difficulty of such
transferable features in coping with both classification and localization
subtasks in object detection. To address this issue, in this paper, we propose
Task-specific Inconsistency Alignment (TIA), by developing a new alignment
mechanism in separate task spaces, improving the performance of the detector on
both subtasks. Specifically, we add a set of auxiliary predictors for both
classification and localization branches, and exploit their behavioral
inconsistencies as finer-grained domain-specific measures. Then, we devise
task-specific losses to align such cross-domain disagreement of both subtasks.
By optimizing them individually, we are able to well approximate the category-
and boundary-wise discrepancies in each task space, and therefore narrow them
in a decoupled manner. TIA demonstrates superior results on various scenarios
to the previous state-of-the-art methods. It is also observed that both the
classification and localization capabilities of the detector are sufficiently
strengthened, further demonstrating the effectiveness of our TIA method. Code
and trained models are publicly available at https://github.com/MCG-NJU/TIA.

Computer Vision and Pattern Recognition