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Abstract
Novel Object Captioning is a zero-shot Image Captioning task requiring describing objects not seen in the training captions, but for which information is available from external object detectors. The key challenge is to select and describe all salient detected novel objects in the input images. In this paper, we focus on this challenge and propose the ECOL-R model (Encouraging Copying of Object Labels with Reinforced Learning), a copy-augmented transformer model that is encouraged to accurately describe the novel object labels. This is achieved via a specialised reward function in the SCST reinforcement learning framework (Rennie et al., 2017) that encourages novel object mentions while maintaining the caption quality. We further restrict the SCST training to the images where detected objects are mentioned in reference captions to train the ECOL-R model. We additionally improve our copy mechanism via Abstract Labels, which transfer knowledge from known to novel object types, and a Morphological Selector, which determines the appropriate inflected forms of novel object labels. The resulting model sets new state-of-the-art on the nocaps (Agrawal et al., 2019) and held-out COCO (Hendricks et al., 2016) benchmarks.
Original language | English |
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Title of host publication | EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference |
Place of Publication | Stroudsburg, PA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1222-1234 |
Number of pages | 13 |
ISBN (Electronic) | 9781954085022 |
Publication status | Published - 2021 |
Event | 16th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2021 - Virtual, Online Duration: 19 Apr 2021 → 23 Apr 2021 |
Publication series
Name | EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference |
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Conference
Conference | 16th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2021 |
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City | Virtual, Online |
Period | 19/04/21 → 23/04/21 |
Bibliographical note
Copyright the Publisher 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Fingerprint
Dive into the research topics of 'ECOL-R: encouraging copying in novel object captioning with reinforcement learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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Improved syntactic and semantic analysis for natural language processing
Johnson, M. & Steedman, M.
30/06/16 → 31/12/21
Project: Research