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Global Contextual Dependency Network for Object Detection

Author

Listed:
  • Junda Li

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China)

  • Chunxu Zhang

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China)

  • Bo Yang

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China)

Abstract

Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpful to recognize objects with featureless appearances and restrain false detections. To tackle the problem, a simple framework, named Global Contextual Dependency Network (GCDN), is presented to enhance the classification ability of two-stage detectors. Our GCDN mainly consists of two components, Context Representation Module (CRM) and Context Dependency Module (CDM). Specifically, a CRM is proposed to construct multi-scale context representations. With CRM, contextual information can be fully explored at different scales. Moreover, the CDM is designed to capture global contextual dependencies. Our GCDN includes multiple CDMs. Each CDM utilizes local Region of Interest (RoI) features and single-scale context representation to generate single-scale contextual RoI features via the attention mechanism. Finally, the contextual RoI features generated by parallel CDMs independently are combined with the original RoI features to help classification. Experiments on MS-COCO 2017 benchmark dataset show that our approach brings continuous improvements for two-stage detectors.

Suggested Citation

  • Junda Li & Chunxu Zhang & Bo Yang, 2022. "Global Contextual Dependency Network for Object Detection," Future Internet, MDPI, vol. 14(1), pages 1-10, January.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:1:p:27-:d:723453
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