计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 151-158.doi: 10.11896/jsjkx.210900159
方仲俊1,2, 张静1, 李冬冬1,2
FANG Zhong-jun1,2, ZHANG Jing1, LI Dong-dong1,2
摘要: 图像描述是图像理解领域的热点研究课题之一,它是结合计算机视觉和自然语言处理的跨媒体数据分析任务,通过理解图像内容并生成语义和语法都正确的句子来描述图像。现有的图像描述方法多采用编码器-解码器模型,该类方法在提取图像中的视觉对象特征时大多忽略了视觉对象之间的相对位置关系,但它对于正确描述图像的内容是非常重要的。基于此,提出了基于Transformer的空间和多层级联合编码的图像描述方法。为了更好地利用图像中所包含的对象的位置信息,提出了视觉对象的空间编码机制,将各个视觉对象独立的空间关系转换为视觉对象间的相对空间关系,以此来帮助模型识别各个视觉对象间的相对位置关系。同时,在视觉对象的编码阶段,顶部的编码特征保留了更多的贴合图像语义信息,但丢失了图像部分视觉信息,考虑到这一点,文中提出了多层级联合编码机制,通过整合各个浅层的编码层所包含的图像特征信息来完善顶部编码层所蕴含的语义的信息,从而获取到更丰富的贴合图像的语义信息的编码特征。文中在MSCOCO数据集上使用多种评估指标(BLEU,METEOR,ROUGE-L和 CIDEr等)对提出的图像描述方法进行评估,并通过消融实验证明了提出的基于空间的编码机制以及多层级联合编码机制能够辅助产生更为准确有效的图像描述语句。对比实验结果表明,所提方法能够产生准确、有效的图像描述并优于大多数最新的算法。
中图分类号:
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