Generating Description for Sequential Images with Local-Object Attention Conditioned on Global Semantic Context
Jing Su, Chenghua Lin, Mian Zhou, Qingyun Dai, Haoyu Lv · 2018
In this paper, we propose an end-toend 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 multilayer 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.