G-CNN: An Iterative Grid Based Object Detector

@article{Najibi2016GCNNAI,
  title={G-CNN: An Iterative Grid Based Object Detector},
  author={Mahyar Najibi and Mohammad Rastegari and Larry S. Davis},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={2369-2377}
}
  • Mahyar Najibi, Mohammad Rastegari, Larry S. Davis
  • Published 2016
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. [...] Key Method We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makes…Expand Abstract

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 77 CITATIONS, ESTIMATED 87% COVERAGE

    Improvement for Fast Object Detection Based on Regression Method

    VIEW 4 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    PBG-Net: Object detection with a multi-feature and iterative CNN model

    A detection method for low-pixel ratio object

    VIEW 1 EXCERPT
    CITES METHODS

    Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection

    VIEW 1 EXCERPT
    CITES METHODS

    Multistage Object Detection With Group Recursive Learning

    VIEW 1 EXCERPT
    CITES BACKGROUND

    FILTER CITATIONS BY YEAR

    2016
    2020

    CITATION STATISTICS

    • 5 Highly Influenced Citations

    • Averaged 19 Citations per year from 2018 through 2020

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 32 REFERENCES

    AttentionNet: Aggregating Weak Directions for Accurate Object Detection

    VIEW 1 EXCERPT

    R-CNN minus R

    VIEW 1 EXCERPT

    Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

    VIEW 1 EXCERPT

    Region-Based Convolutional Networks for Accurate Object Detection and Segmentation

    Fast R-CNN

    • Ross B. Girshick
    • Computer Science
    • 2015 IEEE International Conference on Computer Vision (ICCV)
    • 2015
    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Scalable Object Detection Using Deep Neural Networks

    VIEW 1 EXCERPT

    You Only Look Once: Unified, Real-Time Object Detection

    VIEW 1 EXCERPT