探索使用众包的深度学习来注释图像

Samreen Anjum, Ambika Verma, B. Dang, D. Gurari
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引用次数: 6

摘要

我们调查了采用混合算法-众包方法比完全依赖算法或人群来注释图像的传统方法产生的好处。我们介绍了一个框架,使用户能够研究三种流行的图像分析任务的不同混合工作流程:图像分类,目标检测和图像字幕。包括三种基于工人的混合方法:(i)验证预测标签,(ii)正确预测标签,以及(iii)注释算法对其预测置信度较低的图像。在这些工作流中采用深度学习算法,因为它们为图像注释任务提供了高性能。每个工作流都是根据来自三个不同数据集(即VOC, MSCOCO, VizWiz)的图像的注释质量和工作时间来评估的。受我们研究结果的启发,我们提供了关于何时以及如何将深度学习与众包结合使用以实现所需的图像注释质量和效率的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Use of Deep Learning with Crowdsourcing to Annotate Images
We investigate what, if any, benefits arise from employing hybrid algorithm-crowdsourcing approaches over conventional approaches of relying exclusively on algorithms or crowds to annotate images.  We introduce a framework that enables users to investigate different hybrid workflows for three popular image analysis tasks: image classification, object detection, and image captioning.   Three hybrid approaches are included that are based on having workers: (i) verify predicted labels, (ii) correct predicted labels, and (iii) annotate images for which algorithms have low confidence in their predictions.  Deep learning algorithms are employed in these workflows since they offer high performance for image annotation tasks.  Each workflow is evaluated with respect to annotation quality and worker time to completion on images coming from three diverse datasets (i.e., VOC, MSCOCO, VizWiz). Inspired by our findings, we offer recommendations regarding when and how to employ deep learning with crowdsourcing to achieve desired quality and efficiency for image annotation.
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