Improvement of Embedding Channel-Wise Activation in Soft-Attention Neural Image Captioning

Yanke Li
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引用次数: 1

Abstract

The paper dives into the topic of image captioning with the soft attention algorithm. We first review relevant works on the captioned topic in terms of background introduction and then explains the original model in details. On top of the plain soft attention model, we propose two approaches for further improvements: SE attention model which adds an extra channel-wise activation layer, and bi-directional attention model that explores two-way attention order feasibility. We implement both methods under limited experiment conditions and in addition swap the original encoder with state-of-art structure. Quantitative results and example demonstrations show that our proposed methods have achieved better performance than baselines. In the end, some suggestions of future work on top of proposed are summarized for a purpose of completeness.
软注意神经图像字幕中嵌入通道激活的改进
本文研究了基于软注意算法的图像字幕问题。我们首先在背景介绍方面回顾了标题主题的相关著作,然后对原始模型进行了详细的解释。在普通软注意模型的基础上,我们提出了两种进一步改进的方法:SE注意模型(增加了额外的通道激活层)和双向注意模型(探索双向注意顺序的可行性)。我们在有限的实验条件下实现了这两种方法,并且用最先进的结构交换了原始编码器。定量结果和实例演示表明,我们提出的方法取得了比基线更好的性能。最后,在此基础上对今后的工作提出了一些建议,以达到完善的目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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