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Feedback LSTM Network Based on Attention for image description generator

Computers, Materials & Continua 2019年第5期59卷 575-589页

作者： Zhaowei Qu Bingyu Cao Xiaoru Wang Fu Li Peirong Xu Luhan Zhang Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering Portland States UniversityPortlandOR 97207-0751USA

images are complex multimedia data which contain rich semantic *** of current image description generator algorithms only generate plain description,with the lack of distinction between primary and secondary object,leading to insufficient high-level semantic and accuracy under public evaluation *** major issue is the lack of effective network on high-level semantic sentences generation,which contains detailed description for motion and state of the principal *** address the issue,this paper proposes the Attention-based Feedback Long Short-Term Memory Network(AFLN).Based on existing codec framework,there are two independent sub tasks in our method:attention-based feedback LSTM network during decoding and the Convolutional Block Attention Module(CBAM)in the coding ***,we propose an attentionbased network to feedback the features corresponding to the generated word from the previous LSTM decoding *** implement feedback guidance through the related field mapping algorithm,which quantifies the correlation between previous word and latter word,so that the main object can be tracked with highlighted detailed ***,we exploit the attention idea and apply a lightweight and general module called CBAM after the last layer of VGG 16 pretraining network,which can enhance the expression of image coding features by combining channel and spatial dimension attention maps with negligible *** experiments on COCO dataset validate the superiority of our network over the state-of-the-art *** scores and actual effects are *** BLEU 4 score increases from 0.291 to 0.301 while the CIDEr score rising from 0.912 to 0.952.

关键词： image description generator feedback LSTM network attention CBAM

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