BIGDATA: F: Collaborative Research: From Visual Data to Visual Understanding

视觉识别领域专注于创建自动理解照片和视频的计算机算法，在过去几年中取得了巨大进展。算法现在可以以合理的精度识别和定位数千个对象，并识别其他可视内容，如场景和活动。例如，现在有智能手机应用程序可以自动筛选用户的照片，找到所有派对照片，或所有汽车照片，或所有日落照片。然而，这些方法所完成的“视觉理解”类型仍然相当肤浅，主要表现为死记硬背，而不是真正的推理。例如，当前的算法很难判断一幅图像是典型的(例如，道路上的汽车)还是不寻常的(例如，天空中的汽车)，或者回答关于照片的简单问题，例如，“人们在看什么？”，“刚刚发生了什么？”“接下来会发生什么？”一个核心问题是，目前的方法缺乏关于照片外世界的数据。为了实现真正的类似人类的视觉理解，计算机将不得不对给定视觉输入所暗示的更广泛的空间、时间、感知和社会背景进行推理。这个项目正在使用大数据来收集关于事件、物理和社会互动的大规模深层语义知识，以及人们如何感知世界和彼此。这项研究的重点是开发获取和表示这种知识的方法，使其广泛适用于一系列视觉理解任务。这将使新的计算机算法能够对视觉世界有更深入、更像人类的理解，并能够在复杂的现实世界情况和环境中有效地发挥作用。例如，如果机器人可以预测一个人在给定情况下可能会做什么，那么机器人就可以更好地帮助这个人完成他们的任务。更广泛的影响将包括可用于各种视觉推理任务的新的公开可用的软件工具和数据。此外，该项目将有一个多管齐下的教育部分，包括将研究的各个方面纳入研究生教学课程、本科生和K-12外联活动，以及特别指导和重点活动，以提高妇女在计算机科学中的地位。该项目的主要技术重点是促进计算机识别努力，以产生对图像和视频的一般人类视觉理解，这可以在以前看不见的数据、看不见的任务和环境中发挥作用。这个项目的目的是开发一个新的大规模知识库，称为可视化Memex，它以多图表示的形式提取和存储数据项之间的大量可视关系集，其中节点对应于数据项，边表示不同类型的关系。这个庞大的知识库将用于Lambda演算驱动的推理引擎，以对全球范围内的可视数据进行推理。此外，该项目将在几个视觉理解任务上测试计算识别算法，这些任务旨在评估视觉理解的各个方面的进展，包括：语言学(通过视觉问答等语言任务评估我们对图像的理解)、纯视觉(通过视觉填空来评估我们对空间背景的理解)、时间(通过预测未来状态来评估我们的时间理解)、物理(通过预测承受能力来评估我们对人-物体和人-场景交互的理解)。数据集、知识库和评估工具将托管在项目网站(http://www.tamaraberg.com/grants/bigdata.html).上

The field of visual recognition, which focuses on creating computer algorithms for automatically understanding photographs and videos, has made tremendous gains in the past few years. Algorithms can now recognize and localize thousands of objects with reasonable accuracy as well as identify other visual content, such as scenes and activities. For instance, there are now smart phone apps that can automatically sift through a user's photos and find all party pictures, or all pictures of cars, or all sunset photos. However, the type of "visual understanding" done by these methods is still rather superficial, exhibiting mostly rote memorization rather than true reasoning. For example, current algorithms have a hard time telling if an image is typical (e.g., car on a road) or unusual (e.g., car in the sky), or answering simple questions about a photograph, e.g., "what are the people looking at?", "what just happened?", "what might happen next?" A central problem is that current methods lack the data about the world outside of the photograph. To achieve true human-like visual understanding, computers will have to reason about the broader spatial, temporal, perceptual, and social context suggested by a given visual input. This project is using big visual data to gather large-scale deep semantic knowledge about how events, physical and social interactions, and how people perceive the world and each other. The research focuses on developing methods to capture and represent this knowledge in a way that makes it broadly applicable to a range of visual understanding tasks. This will enable novel computer algorithms that have a deeper, more human-like, understanding of the visual world and can effectively function in complex, real-world situations and environments. For example, if a robot can predict what a person might do next in a given situation, then the robot can better aid the person in their task. Broader impacts will include new publicly-available software tools and data that can be used for various visual reasoning tasks. Additionally, the project will have a multi-pronged educational component, including incorporating aspects of the research in the graduate teaching curriculum, undergraduate and K-12 outreach, as well as special mentoring and focused events for advancement of women in computer science.The main technical focus of this project is to advance computational recognition efforts toward producing a general human-like visual understanding of images and video that can function on previously unseen data, unseen tasks and settings. The aim of this project is to develop a new large-scale knowledge base called the visual Memex that extracts and stores vast set of visual relationships between data items in a multi-graph representation, with nodes corresponding to data items and edges indicating different types of relationships. This large knowledge base will be used in a lambda-calculus-powered reasoning engine to make inferences about visual data on a global scale. Additionally, the project will test computational recognition algorithms on several visual understanding tasks designed to evaluate progress on a variety of aspects of visual understanding, including: linguistic (evaluating our understanding about imagery through language tasks such as visual question-answering), to purely visual (evaluating our understanding of spatial context through visual fill-in-the-blanks), to temporal (evaluating our temporal understanding by predicting future states), to physical (evaluating our understanding of human-object and human-scene interactions by predicting affordances). Datasets, knowledge base, and evaluation tools will be hosted on the project web site (http://www.tamaraberg.com/grants/bigdata.html).