Corpus ID: 227014514

Your "Labrador" is My "Dog": Fine-Grained, or Not

  title={Your "Labrador" is My "Dog": Fine-Grained, or Not},
  author={Dongliang Chang and Kaiyue Pang and Yixiao Zheng and Zhanyu Ma and Yi-Zhe Song and Jun Guo},
  • Dongliang Chang, Kaiyue Pang, +3 authors Jun Guo
  • Published 2020
  • Computer Science
  • ArXiv
  • Whether what you see in Figure 1 is a "labrador" or a "dog", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just "dog" would probably suffice. The real question is therefore -- how can we tailor for different fine-grained definitions under divergent levels of expertise. For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal… CONTINUE READING

    Figures and Tables from this paper


    Learning to Navigate for Fine-grained Classification
    • 111
    • Highly Influential
    • PDF
    Picking Deep Filter Responses for Fine-Grained Image Recognition
    • 228
    • PDF
    Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes
    • 10
    • PDF
    Deep Learning for Fine-Grained Image Analysis: A Survey
    • 19
    • PDF
    Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition
    • 74
    • PDF
    Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation
    • 104
    • PDF
    Interpretable and Accurate Fine-grained Recognition via Region Grouping
    • Zixuan Huang, Yanchao Li
    • Computer Science
    • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2020
    • 6
    • PDF
    POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation
    • T. Berg, P. Belhumeur
    • Computer Science
    • 2013 IEEE Conference on Computer Vision and Pattern Recognition
    • 2013
    • 305
    • PDF
    Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval
    • 2
    • PDF
    The application of two-level attention models in deep convolutional neural network for fine-grained image classification
    • 480
    • PDF