Exploring Categorical Regularization for Domain Adaptive Object Detection

by   Chang-Dong Xu, et al.

In this paper, we tackle the domain adaptive object detection problem, where the main challenge lies in significant domain gaps between source and target domains. Previous work seeks to plainly align image-level and instance-level shifts to eventually minimize the domain discrepancy. However, they still overlook to match crucial image regions and important instances across domains, which will strongly affect domain shift mitigation. In this work, we propose a simple but effective categorical regularization framework for alleviating this issue. It can be applied as a plug-and-play component on a series of Domain Adaptive Faster R-CNN methods which are prominent for dealing with domain adaptive detection. Specifically, by integrating an image-level multi-label classifier upon the detection backbone, we can obtain the sparse but crucial image regions corresponding to categorical information, thanks to the weakly localization ability of the classification manner. Meanwhile, at the instance level, we leverage the categorical consistency between image-level predictions (by the classifier) and instance-level predictions (by the detection head) as a regularization factor to automatically hunt for the hard aligned instances of target domains. Extensive experiments of various domain shift scenarios show that our method obtains a significant performance gain over original Domain Adaptive Faster R-CNN detectors. Furthermore, qualitative visualization and analyses can demonstrate the ability of our method for attending on the key regions/instances targeting on domain adaptation. Our code is open-source and available at <https://github.com/Megvii-Nanjing/CR-DA-DET>.


page 1

page 2

page 4

page 7

page 8


Domain Adaptive Faster R-CNN for Object Detection in the Wild

Object detection typically assumes that training and test data are drawn...

MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection

Cross-domain object detection is challenging, and it involves aligning l...

Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment

Domain adaptive detection aims to improve the generalization of detector...

Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection

Object detectors are usually trained with large amount of labeled data, ...

Multi-Granularity Alignment Domain Adaptation for Object Detection

Domain adaptive object detection is challenging due to distinctive data ...

Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection

In order to robustly deploy object detectors across a wide range of scen...

Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation

Current methods of blended targets domain adaptation (BTDA) usually infe...

Please sign up or login with your details

Forgot password? Click here to reset