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arXiv · 2012.01295

Generating Descriptions for Sequential Images with Local-Object Attention and Global Semantic Context Modelling

Jing Su·Chenghua Lin·Mian Zhou·Qingyun Dai·Haoyu Lv
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Abstract

In this paper, we propose an end-to-end CNN-LSTM model for generating descriptions for sequential images with a local-object attention mechanism. To generate coherent descriptions, we capture global semantic context using a multi-layer perceptron, which learns the dependencies between sequential images. A paralleled LSTM network is exploited for decoding the sequence descriptions. Experimental results show that our model outperforms the baseline across three different evaluation metrics on the datasets published by Microsoft.

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Jing Su, Chenghua Lin, Mian Zhou, Qingyun Dai, Haoyu Lv. 2020-12-02. Generating Descriptions for Sequential Images with Local-Object Attention and Global Semantic Context Modelling. https://arxiv.org/abs/2012.01295

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