提高用户标注要求反而提升数据量和满意度，验证了激励设计的有效性。
Design and Evaluation of Camera-Centric Mobile Crowdsourcing Applications
- 设计三种标注强度不同的相机众包应用，对比用户贡献行为。
- 高标注要求版本收集图像最多，且用户满意度未下降。
- 额外标注数据提升了图像检索任务性能，适合数据驱动研究者。
计算机视觉与机器学习中的自动化方法（如图像检索、细粒度识别）所需数据常来自众包。在依赖用户内在动机的场景中，我们探究应用设计如何影响用户贡献意愿及所采集数据的数量与质量。本研究设计了三种基于摄像头的移动众包应用，其用户标注工作量各不相同，并通过用户实验评估了标注需求程度与所获标注图像数量、质量之间的权衡关系。结果表明，更高的标注要求并未导致贡献减少；用户在标注要求最高的版本中收集并标注了最多的图像，且满意度无下降。初步实验显示，额外的标注数据显著提升了图像检索任务的性能。
原文摘要 · Abstract (English)
The data that underlies automated methods in computer vision and machine learning, such as image retrieval and fine-grained recognition, often comes from crowdsourcing. In contexts that rely on the intrinsic motivation of users, we seek to understand how the application design affects a user's willingness to contribute and the quantity and quality of the data they capture. In this project, we designed three versions of a camera-based mobile crowdsourcing application, which varied in the amount of labeling effort requested of the user and conducted a user study to evaluate the trade-off between the level of user-contributed information requested and the quantity and quality of labeled images collected. The results suggest that higher levels of user labeling do not lead to reduced contribution. Users collected and annotated the most images using the application version with the highest requested level of labeling with no decrease in user satisfaction. In preliminary experiments, the additional labeled data supported increased performance on an image retrieval task.
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