作为辅助技术的图像字幕生成：从VizWiz 2020挑战赛中获得的经验教训
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
AI总结：
针对现有竞赛数据集场景匮乏导致图像字幕系统辅助视障人士实用性不足的问题，本文介绍了VizWiz 2020图像字幕挑战赛获奖方案的理论与工程实现，助力改进辅助型图像字幕系统。
摘要：
图像字幕生成技术近期取得了显著进展，这在很大程度上得益于在MS-COCO等精选数据集上训练的神经网络算法的引入。该领域的研究工作通常以将字幕系统部署到实际应用中为愿景驱动。然而，许多竞赛数据集存在数据和场景匮乏的问题，使得在这些数据集上训练的系统作为辅助技术在真实场景（例如帮助视障人士导航和完成日常任务）中的实用性受限。这一差距推动了全新的VizWiz数据集的推出，该数据集包含由视障人士拍摄的图像，以及带有实用的、面向任务信息的字幕。为了帮助机器学习计算机视觉领域实现其产出具有积极社会影响的技术的愿景，VizWiz数据集的策划方举办了多项竞赛，其中包括一项图像字幕生成竞赛。本工作详细介绍了我们在2020年字幕竞赛中获奖方案的理论与工程实现。我们的工作朝着改进辅助型图像字幕生成系统的方向迈出了一步。
英文摘要：
Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO. Often work in this field is motivated by the promise of deployment of captioning systems in practical applications. However, the scarcity of data and contexts in many competition datasets renders the utility of systems trained on these datasets limited as an assistive technology in real-world settings, such as helping visually impaired people navigate and accomplish everyday tasks. This gap motivated the introduction of the novel VizWiz dataset, which consists of images taken by the visually impaired and captions that have useful, task-oriented information. In an attempt to help the machine learning computer vision field realize its promise of producing technologies that have positive social impact, the curators of the VizWiz dataset host several competitions, including one for image captioning. This work details the theory and engineering from our winning submission to the 2020 captioning competition. Our work provides a step towards improved assistive image captioning systems.
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。
