Gated Hierarchical Attention for Image Captioning
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Computer Vision – ACCV 2018 |
Subtitle of host publication | 14th Asian Conference on Computer Vision: Revised Selected Papers |
Editors | C. V. Jawahar, Hongdong Li, Greg Mori, Konrad Schindler |
Pages | 21-37 |
Volume | Part IV |
ISBN (electronic) | 978-3-030-20870-7 |
Publication status | Published - Dec 2018 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 11364 |
ISSN (Print) | 0302-9743 |
ISSN (electronic) | 1611-3349 |
Conference
Title | 14th Asian Conference on Computer Vision (ACCV 2018) |
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Location | |
Place | Australia |
City | Perth |
Period | 2 - 6 December 2018 |
Link(s)
Abstract
Attention modules connecting encoder and decoders have been widely applied in the field of object recognition, image captioning, visual question answering and neural machine translation, and significantly improves the performance. In this paper, we propose a bottom-up gated hierarchical attention (GHA) mechanism for image captioning. Our proposed model employs a CNN as the decoder which is able to learn different concepts at different layers, and apparently, different concepts correspond to different areas of an image. Therefore, we develop the GHA in which low-level concepts are merged into high-level concepts and simultaneously low-level attended features pass to the top to make predictions. Our GHA significantly improves the performance of the model that only applies one level attention, e.g., the CIDEr score increases from 0.923 to 0.999, which is comparable to the state-of-the-art models that employ attributes boosting and reinforcement learning (RL). We also conduct extensive experiments to analyze the CNN decoder and our proposed GHA, and we find that deeper decoders cannot obtain better performance, and when the convolutional decoder becomes deeper the model is likely to collapse during training.
Research Area(s)
- Hierarchical attention, Image captioning, Convolutional decoder
Citation Format(s)
Gated Hierarchical Attention for Image Captioning. / Wang, Qingzhong; Chan, Antoni B.
Computer Vision – ACCV 2018: 14th Asian Conference on Computer Vision: Revised Selected Papers. ed. / C. V. Jawahar; Hongdong Li; Greg Mori; Konrad Schindler. Vol. Part IV 2018. p. 21-37 (Lecture Notes in Computer Science; Vol. 11364).
Computer Vision – ACCV 2018: 14th Asian Conference on Computer Vision: Revised Selected Papers. ed. / C. V. Jawahar; Hongdong Li; Greg Mori; Konrad Schindler. Vol. Part IV 2018. p. 21-37 (Lecture Notes in Computer Science; Vol. 11364).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review