Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
| Approach: | They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words. |
| Outcome: | The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA . |
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| Challenge: | Existing image captioning models rely on object detection features to generate image descriptions, but they are noisy. |
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| Challenge: | Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images, but they introduce noise and it is unclear the effect of parser errors on captioning accuracy. |
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| Challenge: | Recent studies have highlighted severe limitations of contrastive learning models in their ability to perform compositional reasoning over objects, attributes, and relations. |
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Zhuang Li, Yuyang Chai, Terry Yue Zhuo, Lizhen Qu, Gholamreza Haffari, Fei Li, Donghong Ji, Quan Hung Tran
| Challenge: | Existing parsers that convert image captions into scene graphs often suffer from errors and inconsistency. |
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| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
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Improving Image Captioning with Better Use of Caption (2020.acl-main)
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| Challenge: | Existing approaches to image captioning focus on visual attention, but many do not. |
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| Challenge: | Existing methods to summarize video content have only considered video and image data, and the trend towards multimodal video summarization is changing. |
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