RI: Small: Probabilistic Hierarchical Models for Multi-Task Visual Recognition

RI：小型：多任务视觉识别的概率分层模型

• 批准号: 1618903
• 负责人: Deva Ramanan
• 金额: $ 45万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618903&HistoricalAwards=false
• 关键词: RI; Small; Probabilistic; Hierarchical; Models

This project studies biologically-inspired architectures for visual recognition. The human visual system can perform a remarkable number of tasks, from estimating the 3D shape of an object that is grasped to inferring subtle differences between two similar makes and models of cars. Such diverse sets of visual tasks are required of a range of autonomous agents, including self-driving cars or humanoid robotics. Such autonomous platforms have the potential to increase general welfare and health of the overall population. This project attempts to build a computational model capable of such diverse visual tasks. Motivated by biological evidence, this project explores the use of feedback logic to enable such computational reasoning. The project provides research opportunities for both undergraduate and graduate students and for increasing diversity in the fields of computer and human vision. This research focuses on development of a unified hierarchical probabilistic model that can be used to solve multiple fine-grained visual tasks. Feedforward hierarchical models, of which the most ubiquitous are Convolutional Neural Nets (CNNs), have demonstrated remarkable performance in recent history. This project introduces hierarchical models for vision-with-scrutiny tasks, such as 3D articulated pose estimation and part segmentation. Rather than focusing on increasing performance on established benchmark performance, this research provides a theoretical framework for analyzing bottom-up (feedforward) CNNs and imbuing them with novel top-down reasoning capabilities. It does so by exploring a link between three dominant but disparate paradigms for visual recognition: feedforward neural models, generative probabilistic models (Boltzmann machines), and discriminative latent-variable models (deformable part models). The models introduced in this proposal allow CNNs to be used for large-scale multi-task learning, where tasks span both coarse-grained tasks (such as rapid scene categorization) and fine-grained tasks (such as 3D articulated pose estimation). By addressing multiple fine-grained tasks with a single hierarchical architecture, resource requirements for memory and speed are vastly decreased, important for embedded visual perception applications such as autonomous robots and vehicles.

该项目研究以生物学启发的体系结构进行视觉识别。人类视觉系统可以执行大量的任务，从估计被掌握的物体的3D形状到推断两个相似的汽车和型号之间的细微差异。如此多样化的视觉任务集需要一系列自动驾驶剂，包括自动驾驶汽车或人形机器人机器人。这样的自主平台有可能增加整体人口的一般福利和健康。该项目试图构建能够采用这种不同视觉任务的计算模型。在生物学证据的推动下，该项目探讨了反馈逻辑的使用来实现这种计算推理。该项目为本科和研究生提供了研究机会，并为计算机和人类视野领域的多样性增加了。 这项研究重点是开发统一的层次概率模型，该模型可用于解决多个细粒度的视觉任务。馈电分层模型，其中最普遍的是卷积神经网（CNN），在最近的历史中表现出了出色的表现。该项目介绍了有关视觉策略任务的层次模型，例如3D表达姿势估计和部分分段。这项研究并没有专注于提高既定基准性能的性能，而是为分析自下而上（前馈）CNN的理论框架，并以新颖的自上而下的推理能力为其赋予它们。它通过探索以视觉识别的三个主要但不同的范式之间的联系来做到这一点：前馈神经模型，生成概率模型（Boltzmann机器）和辨别性潜在可变性模型（可变形零件模型）。此提案中介绍的模型允许CNN用于大规模多任务学习，其中任务涵盖了粗粒度的任务（例如快速场景分类）和细粒度的任务（例如3D明显的姿势估计）。通过通过单个分层体系结构来解决多个细粒度的任务，对内存和速度的资源要求大大降低，对于嵌入式视觉感知应用程序（例如自动机器人和车辆）来说很重要。