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摘要: 图像描述(Image captioning)是一个融合了计算机视觉和自然语言处理这两个领域的研究方向, 本文为图像描述设计了一种新颖的显著性特征提取机制(Salient feature extraction mechanism, SFEM), 能够在语言模型预测每一个单词之前快速地向语言模型提供最有价值的视觉特征来指导单词预测, 有效解决了现有方法对视觉特征选择不准确以及时间性能不理想的问题. SFEM包含全局显著性特征提取器和即时显著性特征提取器这两个部分: 全局显著性特征提取器能够从多个局部视觉向量中提取出显著性视觉特征, 并整合这些特征到全局显著性视觉向量中; 即时显著性特征提取器能够根据语言模型的需要, 从全局显著性视觉向量中提取出预测每一个单词所需的显著性视觉特征. 本文在MS COCO (Microsoft common objects in context)数据集上对SFEM进行了评估, 实验结果表明SFEM能够显著提升基准模型 (baseline)生成图像描述的准确性, 并且SFEM在生成图像描述的准确性方面明显优于广泛使用的空间注意力模型, 在时间性能上也大幅领先空间注意力模型.Abstract: Image captioning is a research direction that combines computer vision and natural language processing. In this paper, a novel saliency feature extraction mechanism (SFEM) is designed to solve several key problems existing in current methods. It can quickly provide the most valuable visual features to the language model before which predict word. And it effectively solves the problems that the existing methods are inaccurate in selecting visual features and time-consuming. SFEM consists of global salient feature extractor and instant salient feature extractor: global salient Feature extractor extracts salient visual features from multiple local visual vectors and integrate these features into a global salient visual vector; the instant salient feature extractor can extract the saliency visual features required at each moment from the global saliency visual vector according to the needs of the language model. We evaluated SFEM on the MS COCO (Microsoft common objects in context) dataset. Experiments show that our SFEM can significantly improve the accuracy of baseline in caption generating. And SFEM is significantly better than the widely used spatial attention model in both the accuracy of generating caption and time performance.
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Key words:
- Image captioning /
- salient feature extract /
- language model /
- encoder /
- decoder
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图 5 即时显著性特征随预测单词的变化
Fig. 5 The change of instant salient features with predicted words
表 1
$\bar{D_{t}}$ 值最高的20个单词Table 1 The top-20 words with
$\bar{D_{t}}$ value单词 $\overline{D}_{t}$ 单词 $\overline{D}_{t}$ 单词 $\overline{D}_{t}$ hood 0.0592 ducks 0.0565 doughnut 0.0546 cats 0.0589 pug 0.0564 baby 0.0546 teddy 0.0576 rug 0.0561 bird 0.0545 little 0.0573 hummingbird 0.0556 pen 0.0543 duck 0.0571 pasta 0.0549 motorcycle 0.0543 bananas 0.0569 horse 0.0547 colorful 0.0542 seagull 0.0565 panda 0.0546 — — 
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表 2 Encoder-Decoder + SFEM在MS COCO数据集上的表现(%)
Table 2 The performance of Encoder-Decoder + SFEM on MS COCO dataset (%)
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表 3 Up-Down + SFEM在MS COCO数据集上的表现(%)
Table 3 The performance of Up-Down + SFEM on MS COCO dataset (%)
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表 4 本模型和空间注意力模型的时间性能对比(帧/s)
Table 4 Time performance comparison between our model and the spatial attention model (frame/s)
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表 5 各个模块单次执行平均花费时间(s)
Table 5 The average time spent by each module in a single execution (s)
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表 7 组合模型在MS COCO数据集上的表现(%)
Table 7 Performance of the combined model on MS COCO dataset (%)
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