Domain-Invariant Disentangled Network for generalizable object detection

Chuang Lin, Zehuan Yuan, Sicheng Zhao, Peize Sun, Changhu Wang, Jianfei Cai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

37 Citations (Scopus)

Abstract

We address the problem of domain generalizable object detection, which aims to learn a domain-invariant detector from multiple “seen” domains so that it can generalize well to other “unseen” domains. The generalization ability is crucial in practical scenarios especially when it is difficult to collect data. Compared to image classification, domain generalization in object detection has seldom been explored with more challenges brought by domain gaps on both image and instance levels. In this paper, we propose a novel generalizable object detection model, termed Domain-Invariant Disentangled Network (DIDN). In contrast to directly aligning multiple sources, we integrate a disentangled network into Faster R-CNN. By disentangling representations on both image and instance levels, DIDN is able to learn domain-invariant representations that are suitable for generalized object detection. Furthermore, we design a cross-level representation reconstruction to complement this two-level disentanglement so that informative object representations could be preserved. Extensive experiments are conducted on five benchmark datasets and the results demonstrate that our model achieves state-of-the-art performances on domain generalization for object detection.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
EditorsDima Damen, Tal Hassner, Chris Pal, Yoichi Sato
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages8751-8760
Number of pages10
ISBN (Electronic)9781665428125
ISBN (Print)9781665428132
DOIs
Publication statusPublished - 2021
EventIEEE International Conference on Computer Vision 2021 - Online, United States of America
Duration: 11 Oct 202117 Oct 2021
https://iccv2021.thecvf.com/home (Website)
https://ieeexplore.ieee.org/xpl/conhome/9709627/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

ConferenceIEEE International Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Country/TerritoryUnited States of America
CityOnline
Period11/10/2117/10/21
Internet address

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