CAREER: Weakly-Supervised Visual Scene Understanding: Combining Images and Videos, and Going Beyond Semantic Tags

互联网提供了无穷无尽的图片和视频，充斥着注释薄弱的元数据，如文本标签、GPS坐标、时间戳或社交媒体情绪。这种巨大的可视数据资源为创建可伸缩且功能强大的识别算法提供了机会，这些算法不依赖于昂贵的人工注释。该项目的研究部分开发了新的视觉场景理解算法，可以有效地从这种弱标注的视觉数据中学习。主要的新奇之处在于将图像和视频结合在一起。开发的算法可能会在包括人工智能、安全和农业科学在内的许多领域产生广泛影响。除科学影响外，该项目还开展补充的教育和外联活动。具体地说，它为高中生、本科生和研究生提供指导，教授加州大学戴维斯分校一直缺乏的新的本科生和研究生计算机视觉课程，并组织了一个关于弱监督视觉场景理解的国际研讨会。该项目开发了新的算法，以通过两种互补的方式促进弱监督视觉场景理解：(1)与图像和视频联合学习，以利用它们的互补性；(2)从超出标准语义标签(如时间戳、字幕和相对比较)的弱监督信号中学习。具体地说，它研究了新的方法来推进任务，如全自动视频对象分割、弱监督对象检测、对象类别的无监督学习、以及挖掘图像/视频数据中与弱监督信号相关的局部模式。在整个项目中，该项目探索了理解和减少弱标签中的噪音以及克服图像和视频之间的领域差异的方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The internet provides an endless supply of images and videos, replete with weakly-annotated meta-data such as text tags, GPS coordinates, timestamps, or social media sentiments. This huge resource of visual data provides an opportunity to create scalable and powerful recognition algorithms that do not depend on expensive human annotations. The research component of this project develops novel visual scene understanding algorithms that can effectively learn from such weakly-annotated visual data. The main novelty is to combine both images and videos together. The developed algorithms could have broad impact in numerous fields including AI, security, and agricultural sciences. In addition to scientific impact, the project performs complementary educational and outreach activities. Specifically, it provides mentorship to high school, undergraduate, and graduate students, teaches new undergraduate and graduate computer vision courses that have been lacking at UC Davis, and organizes an international workshop on weakly-supervised visual scene understanding.This project develops novel algorithms to advance weakly-supervised visual scene understanding in two complementary ways: (1) learning jointly with both images and videos to take advantage of their complementarity, and (2) learning from weak supervisory signals that go beyond standard semantic tags such as timestamps, captions, and relative comparisons. Specifically, it investigates novel approaches to advance tasks like fully-automatic video object segmentation, weakly-supervised object detection, unsupervised learning of object categories, and mining of localized patterns in the image/video data that are correlated with the weak supervisory signal. Throughout, the project explores ways to understand and mitigate noise in the weak labels and to overcome the domain differences between images and videos.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.