On Label Granularity and Object Localization
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2022 : 17th European Conference, Proceedings |
Editors | Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner |
Number of pages | 17 |
Publisher | Springer |
Publication date | 2022 |
Pages | 604-620 |
ISBN (Print) | 9783031200793 |
DOIs | |
Publication status | Published - 2022 |
Event | 17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022 |
Conference
Conference | 17th European Conference on Computer Vision, ECCV 2022 |
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Land | Israel |
By | Tel Aviv |
Periode | 23/10/2022 → 27/10/2022 |
Series | Lecture Notes in Computer Science |
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Volume | 13670 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Links
- https://arxiv.org/pdf/2207.10225
Submitted manuscript
ID: 342672283