Conference item
Many-shot from low-shot: learning to annotate using mixed supervision for object detection
- Abstract:
-
Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperform with respect to their strongly supervised counterpart, as weak training signals often result in partial or oversized detections. Towards solving this problem we introduce, for the first time, an...
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- Publication status:
- Published
- Peer review status:
- Reviewed (other)
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Bibliographic Details
- Publisher:
- Springer Nature Publisher's website
- Host title:
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Series:
- Lecture Notes in Computer Science
- Journal:
- Proceedings of the 16th European Conference on Computer Vision (ECCV 2020) Journal website
- Volume:
- 12353
- Pages:
- 35-50
- Publication date:
- 2020-11-07
- Acceptance date:
- 2020-07-02
- Event title:
- European Conference on Computer Vision (ECCV 2020)
- Event location:
- Online
- Event website:
- https://eccv2020.eu/
- Event start date:
- 2021-08-23
- Event end date:
- 2021-08-28
- DOI:
- EISSN:
-
1611-3349
- ISSN:
-
0302-9743
- EISBN:
- 978-3-030-58598-3
- ISBN:
- 9783030585976
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1151096
- Local pid:
- pubs:1151096
- Deposit date:
- 2021-01-05
Terms of use
- Copyright holder:
- Springer
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020
- Notes:
- This paper was presented at the 16th European Conference on Computer Vision (ECCV 2020), 23rd - 28th August 2020. This is the accepted manuscript version of the article. The final version is available from Springer at: https://doi.org/10.1007/978-3-030-58598-3_3
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