EAGER: Diverse M-Best Predictions from Probabilistic Models

计算机视觉系统必须处理显著程度的模糊性——从物体之间和内部的遮挡以及不同的外观、照明和姿势。概率模型为处理不确定性和将证据转化为对世界的后验信念提供了一个原则性框架。通常，视觉系统使用这种信念来预测“最可能”或最大后验假设。不幸的是，我们目前的模型是不准确的，这种单一最佳假设往往是不正确的。该项目探索了一种新颖的方法，允许视觉系统通过产生多个合理的假设来对冲不确定性。具体来说，该项目开发了从概率模型中寻找各种高概率解决方案的技术。该项目侧重于(a)交互式对象切割（其中向用户显示多个分段，以加速收敛到可接受的结果）；(b)语义分割（其中多个可信的场景标签被传播到级联的后续阶段进行高阶处理）；(c)人/物体跟踪（每帧上的多个定位假设减少了序列跟踪器的搜索空间）。这个项目在概率推理的背景下产生了新的科学知识，并推动了计算机视觉的发展。所开发的技术对语音和自然语言处理等其他人工智能领域也很有用。PI和他的学生们通过组织研讨会、教程和期刊特刊，以及公开分享代码和结果，广泛地传播生产出来的作品。该项目让本科生和女性参与计算机科学研究。

Computer Vision systems must deal with significant levels of ambiguity - from inter- and intra-object occlusion and varying appearance, lighting, and pose. Probabilistic models provide a principled framework for dealing with uncertainty and for converting evidence into a posteriori belief about the world. Typically, a vision system uses this belief to predict the "most likely" or maximum a-posteriori hypothesis. Unfortunately, our current models are inaccurate and this single-best hypothesis is often incorrect. This project explores a novel way to allow vision systems to hedge against uncertainty by producing multiple plausible hypotheses. Specifically, this project develops techniques for finding a diverse set of high-probability solutions from probabilistic models. The project focuses on (a) interactive object cutout (where multiple segmentations are shown to the user to expedite convergence to an acceptable result); (b) semantic segmentation (where multiple plausible scene labelings are propagated to subsequent stages of a cascade for higher-order processing); (c) person/object tracking (where multiple localization hypotheses on each frame reduce the search space of a sequence tracker). This project is producing new scientific knowledge in the context of probabilistic reasoning and advancing the state of art in computer vision. The techniques developed are useful for other AI domains such as Speech and Natural Language Processing. The PI and his students are broadly disseminating produced work by organizing workshops, tutorials, and journal special issues, and publicly sharing code and results. The project is engaging undergraduate students and women in computer science research.