Google Research

On Label Granularity and Object Localization

  • Elijah Henry John Cole
  • Kimberly Wilber
  • Grant Van Horn
  • Xuan Yang
  • Marco Fornoni
  • Pietro Perona
  • Serge Belongie
  • Andrew Howard
  • Mac Aodha, Oisin
European Conference on Computer Vision, Springer (2022), pp. 604-620


Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled at different levels of granularity. Is it an animal, a bird, or a great horned owl? Which image-level labels should we use? In this paper we study the role of label granularity in WSOL. To facilitate this investigation we introduce iNatLoc500, a new large-scale fine-grained benchmark dataset for WSOL. Surprisingly, we find that choosing the right training label granularity provides a much larger performance boost than choosing the best WSOL algorithm. We also show that changing the label granularity can significantly improve data efficiency.

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