神经双胞胎对话和替代计算
Neural Twins Talk & Alternative Calculations
摘要：
受人类大脑在描述高度集中的主题时如何使用更多神经通路的启发，我们表明用于图像描述的主要视觉语言任务的深度注意模型可以扩展以实现更好的性能。图像字幕是计算机视觉和自然语言处理之间的桥梁。自动化的图像描述是一种工具，可以消除人工为未见过的图像创建描述性字幕的需要。自动化的图像描述具有挑战性，但也很有趣。其中一个原因是，能够生成描述输入图像的句子的基于AI的系统可以用于各种各样的任务，而不仅仅是为在网络上找到的或上传到社交媒体的看不见的图像生成标题。例如，在生物学和医学科学中，这些系统可以为研究人员和医生提供相关图像的简短语言描述，从而可能加快他们的工作。
英文摘要：
Inspired by how the human brain employs a higher number of neural pathways when describing a highly focused subject, we show that deep attentive models used for the main vision-language task of image captioning, could be extended to achieve better performance. Image captioning bridges a gap between computer vision and natural language processing. Automated image captioning is used as a tool to eliminate the need for human agent for creating descriptive captions for unseen images.Automated image captioning is challenging and yet interesting. One reason is that AI based systems capable of generating sentences that describe an input image could be used in a wide variety of tasks beyond generating captions for unseen images found on web or uploaded to social media. For example, in biology and medical sciences, these systems could provide researchers and physicians with a brief linguistic description of relevant images, potentially expediting their work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
