Cross-domain Object Detection Via Feature Disentanglement - ArXiv

Computer Science > Computer Vision and Pattern Recognition arXiv:2201.01929 (cs) [Submitted on 6 Jan 2022] Title:Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement Authors:Dongnan Liu, Chaoyi Zhang, Yang Song, Heng Huang, Chenyu Wang, Michael Barnett, Weidong Cai View a PDF of the paper titled Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement, by Dongnan Liu and 6 other authors View PDF

Abstract:Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging the domain distribution gaps. For the UDA-based cross-domain object detection methods, the majority of them alleviate the domain bias by inducing the domain-invariant feature generation via adversarial learning strategy. However, their domain discriminators have limited classification ability due to the unstable adversarial training process. Therefore, the extracted features induced by them cannot be perfectly domain-invariant and still contain domain-private factors, bringing obstacles to further alleviate the cross-domain discrepancy. To tackle this issue, we design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module, respectively. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.

Comments:	Accepted to appear in IEEE Transactions on Multimedia; source code: this https URL
Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2201.01929 [cs.CV]
(or arXiv:2201.01929v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2201.01929 Focus to learn more arXiv-issued DOI via DataCite

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From: Dongnan Liu [view email] [v1] Thu, 6 Jan 2022 05:43:01 UTC (12,701 KB) Full-text links:

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