SIG-011: International Workshop on Stochastic Image Grammars

这笔旅费资助两名美国参与者参加随机图像语法国际研讨会（SIG-11）。随机图像语法的概念包括图像和视频中发生的对象和事件的分层表示，以及它们相关的学习和推理算法。图像语法的优点在于它们的表达能力，通过使用相对小得多的词汇表和一些组合规则来表示指数级数量的对象和事件配置。统计学、机器学习、自然语言处理和认知心理学经历了随机语法的复兴。然而，在计算机视觉领域，这种势头似乎只出现在二维物体识别领域。研讨会的主要目标是促进这些传统上独立的科学学科之间的跨学科研究，以基于语法的公式解决更广泛的视觉问题，超越物体识别，例如，从运动到3D结构，以及活动识别。该研讨会还旨在通过解决对统一理论框架的需求，减少图像语法研究小组之间明显的脱节。为此，SIG-11提供了一个论坛，供视觉社区和认知心理学、神经科学和自然语言处理方面的专家主讲人分享语法方面的研究经验。邀请同行评议的论文预计将在第13届国际计算机视觉会议论文集上发表。

This travel grant supports two US participants to attend the International Workshop on Stochastic Image Grammars (SIG-11). The notion of stochastic image grammars encompasses hierarchical representations of objects and events occurring in images and video, and their associated learning and inference algorithms. The virtue of image grammars lies in their expressive power to represent an exponentially large number of object and event configurations by using a relatively much smaller vocabulary, and a few compositional rules. Statistics, machine learning, natural language processing, and cognitive psychology experience a resurgence of stochastic grammars. In computer vision, however, this momentum seems to be present only in the area of 2D object recognition. The main objective of the workshop is to promote interdisciplinary research among these traditionally separate scientific disciplines toward grammar-based formulations of a wider range of vision problems, beyond object recognition, such as, e.g., 3D structure from motion, and activity recognition. The workshop is also aimed at reducing the apparent disconnect between research groups working on image grammars, by addressing the need for a unified theoretical framework. To this end, SIG-11 provides a forum for sharing research experiences in grammars between the vision community and the keynote speakers who are experts in cognitive psychology, neuroscience, and natural language processing. Solicited peer-reviewed papers are expected to be published in the proceedings of the 13th International Conference on Computer Vision.