Cole, Elijah and Wilber, Kimberly and Van Horn, Grant and Yang, Xuan and Fornoni, Marco and Perona, Pietro and Belongie, Serge and Howard, Andrew and Mac Aodha, Oisin (2022) On Label Granularity and Object Localization. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20221219-234038678
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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.
| Item Type: | Report or Paper (Discussion Paper) | ||||||||||||||||
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| Additional Information: | We thank the iNaturalist community for sharing images and species annotations. This work was supported by the Caltech Resnick Sustainability Institute, an NSF Graduate Research Fellowship (grant number DGE1745301), and the Pioneer Centre for AI (DNRF grant number P1). | ||||||||||||||||
| Group: | Resnick Sustainability Institute | ||||||||||||||||
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| DOI: | 10.48550/arXiv.2207.10225 | ||||||||||||||||
| Record Number: | CaltechAUTHORS:20221219-234038678 | ||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20221219-234038678 | ||||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||
| ID Code: | 118461 | ||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||
| Deposited By: | George Porter | ||||||||||||||||
| Deposited On: | 21 Dec 2022 16:54 | ||||||||||||||||
| Last Modified: | 02 Jun 2023 01:28 |
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