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arXiv · 2207.10225

On Label Granularity and Object Localization

Elijah Cole·Kimberly Wilber·Grant Van Horn·Xuan Yang·Marco Fornoni·Pietro Perona·Serge Belongie·Andrew Howard·Oisin Mac Aodha
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Abstract

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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Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang, Marco Fornoni, Pietro Perona, Serge Belongie, Andrew Howard, Oisin Mac Aodha. 2022-07-20. On Label Granularity and Object Localization. https://arxiv.org/abs/2207.10225

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