Towards a Compositional Generative Model of Human Vision

迈向人类视觉的组合生成模型

• 批准号: 10018020
• 负责人: DANIEL J KERSTEN
• 金额: $ 33.29万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018020
• 关键词: Affect; Area; Articulation; Behavioral; Body Image; Body part; Brain; Brain imaging; Complex; Computer Vision Systems; Confusion; Cues; Data; Development; Disease; Elbow; Feedback; Functional Magnetic Resonance Imaging; Future; Goals; Human; Human body; Hybrids; Image; Impairment; Individual; Knowledge; Link; Measures; Modeling; Perception; Performance; Predictive Value; Process; Psychophysics; Published Comment; Research; Structure; System; Testing; Training; Vision; Visual; Visual system structure; Work; Wrist; base; convolutional neural network; crowdsourcing; human model; imaging modality; improved; object recognition; relating to nervous system; spatial relationship; success; theories; vision science

Understanding object recognition has long been a central problem in vision science, because of its applied utility and computational difficulty. Progress has been slow, because of an inability to process complex natural images, where the largest challenges arise. Recently, advances in Deep Convolutional Neural Networks (DCNNs) spurred unprecedented success in natural image recognition. The general goal of this proposal is to leverage this success to test computational theories of human object recognition in natural images. However, DCNNs still markedly underperform humans when challenged with high levels of ambiguity, occlusion, and articulation. We hypothesize that humans' superior performance arises from the use of knowledge about how images and objects are structured. Preliminary evidence for this claim comes from the success of hybrid models, that combine DCNNS for identifying features and parts in images, with explicit knowledge of object and image structure. These computations occur within a hierarchy, which includes both top-down and bottom- up processing. The specific goal of the work proposed here is to strongly test whether these computational strategies, structured, hierarchical representations and bidirectional processing, are used to recognize objects in natural images. Human bodies are composed of hierarchically organized configurable parts, making them an ideal test domain. We examine the complete recognition process, from parts, to pairs of parts, to whole bodies, each in its own aim. Each aim also tests important sub-hypotheses about when and how the computational strategies are used. Aim 1 examines recognition of individual body parts, testing whether it is dependent on parsing images into more basic features and relationships, for example edges and materials. Aim 2 examines pairs of parts, testing the importance of knowledge of body connectedness relationships. Aim 3 examines perception of entire bodies, testing whether knowledge of global body structure guides bidirectional processing. In each aim, we first develop nested computer vision models that either do or do not make use of structural knowledge, to test whether it aids recognition. We then test whether human performance can be accounted for by the availability of that structural knowledge. We next measure neural activity with functional MRI to identify where and how it is used in cortex. Finally, we integrate these results to produce even stronger tests, using the nested models to predict human performance and confusion matrices as well as fMRI activity levels and confusion matrices. Altogether, this work will strongly test key theoretical accounts of object recognition in the most important domain, perception of natural images. The work, based on extensive preliminary data, measures and models the entire body recognition system. The models developed and tested here should surpass the state-of-the-art, and be useful for many real-world recognition tasks. The proposal will also lay the groundwork for future studies of recognition impaired by disease.

理解对象识别长期以来一直是视觉科学的核心问题，因为它的应用 实用程序和计算困难。进步很慢，因为无法处理复杂的自然 图像，最大的挑战是出现的。最近，深度卷积神经网络的进步 （DCNNS）在自然图像识别方面刺激了前所未有的成功。该提议的一般目标是 利用这种成功来测试自然图像中人类物体识别的计算理论。然而， 当受到高水平的歧义，遮挡和 关节。我们假设人类的出色表现是由于使用有关如何的知识而产生的 图像和对象是结构化的。该主张的初步证据来自混合动力的成功 模型，将DCNN结合在一起，以识别图像中的特征和零件，并明确地了解对象 和图像结构。这些计算发生在层次结构中，其中包括自上而下和底部 - 加工。这里提出的工作的具体目标是强烈测试这些计算是否是否 策略，结构化，分层表示和双向处理，用于识别对象 在自然图像中。人体由层次有组织的可配置零件组成，使其成为 理想的测试域。我们检查了从零件到成对零件到整个身体的完整识别过程， 每个都以其自己的目标。每个目标还测试了有关计算何时以及如何以及如何计算的重要子集 使用策略。 AIM 1检查对各个身体部位的识别，测试它是否取决于 将图像解析为更基本的特征和关系，例如边缘和材料。 AIM 2检查 成对的零件，测试身体联系关系知识的重要性。 AIM 3检查 对整个身体的感知，测试全球身体结构的知识是否指导双向处理。 在每个目标中，我们都首先开发嵌套的计算机视觉模型，它们可以使用或不利用结构 知识，测试它是否有助于识别。然后，我们测试是否可以考虑人类绩效 通过这种结构知识的可用性。我们接下来用功能性MRI测量神经活动以识别 皮质中的位置以及如何使用。最后，我们将这些结果整合在一起，以产生更强的测试 嵌套模型以预测人类的性能和混乱矩阵以及fMRI活动水平以及 混乱矩阵。总的来说，这项工作将强烈测试对象识别的关键理论说明 最重要的领域，自然图像的感知。这项工作基于广泛的初步数据 衡量和建模整个身体识别系统。在这里开发和测试的模型应该 超越最先进的方法，对于许多现实世界识别任务非常有用。该提议还将提出 疾病障碍障碍的未来识别研究的基础。