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Domain Attention Model for Domain Generalization in Object Detection

  • Weixiong He
  • Huicheng Zheng
  • Jianhuang Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11259)

Abstract

Domain generalization methods in object detection aim to learn a domain-invariant detector for different domains. However, it is difficult to obtain a domain-invariant detector when there is large discrepancy between different domains. Based on the idea of biasing the allocation of available processing resources towards the most informative components of an input, attention models have shown promising performance on different tasks. In this paper, we provide a framework for addressing the issue of visual domain generalization with domain attention. Specifically, we build a domain attention block utilizing the source domain discrepancy to learn different weights for different source domains on the input features, so that the input features similar to the source domains will be enhanced and the features different from all the source domains will be suppressed. Thus we can obtain a domain-general representation effective for localization and classification in the proposed model. In order to demonstrate the merits of the proposed approach, we put forward a HD-16 dataset for object detection in different scenes. Extensive experiments on HD-16 dataset verify the effectiveness of the proposed approach.

Keywords

Domain generalization Object detection Attention model 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China (U1611461), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase, No. U1501501), and Science and Technology Program of Guangzhou (No. 201803030029).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Weixiong He
    • 1
    • 2
    • 3
  • Huicheng Zheng
    • 1
    • 2
    • 3
  • Jianhuang Lai
    • 1
    • 2
    • 3
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of EducationGuangzhouChina
  3. 3.Guangdong Key Laboratory of Information Security TechnologyGuangzhouChina

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