Look and modify: Modification networks for image captioning


Samani, Fawaz and Elsayed, Mahmoud (2020) Look and modify: Modification networks for image captioning. Computer Vision and Pattern Recognition. pp. 1-12.

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Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to modify existing captions from a given framework by modeling the residual information, where at each timestep the model learns what to keep, remove or add to the existing caption allowing the model to fully focus on "what to modify" rather than on "what to predict". We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.

Item Type: Article
Uncontrolled Keywords: Image authentication
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 16 Dec 2020 07:54
Last Modified: 16 Dec 2020 07:54
URII: http://shdl.mmu.edu.my/id/eprint/7874


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