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

这个项目研究生物启发的视觉识别架构。人类的视觉系统可以执行大量的任务，从估计被抓住的物体的3D形状到推断两个相似品牌和型号的汽车之间的细微差异。这种多样化的视觉任务是一系列自主代理所必需的，包括自动驾驶汽车或类人机器人。这种自主平台有可能增加总体福利和整体人口的健康。这个项目试图建立一个能够处理各种视觉任务的计算模型。在生物证据的推动下，该项目探索了使用反馈逻辑来实现这种计算推理。该项目为本科生和研究生提供了研究机会，并增加了计算机和人类视觉领域的多样性。本研究的重点是开发一个统一的分层概率模型，该模型可用于解决多个细粒度的视觉任务。前馈层次模型，其中最普遍的是卷积神经网络（cnn），在最近的历史中表现出了显着的性能。该项目引入了用于视觉检查任务的分层模型，例如3D关节姿态估计和零件分割。本研究不是专注于在已有的基准性能上提高性能，而是提供了一个理论框架来分析自下而上（前馈）的cnn，并赋予它们新的自上而下的推理能力。它通过探索视觉识别三种主要但不同的范式之间的联系来实现这一目标：前馈神经模型、生成概率模型（玻尔兹曼机）和判别潜变量模型（可变形部件模型）。本提案中引入的模型允许cnn用于大规模的多任务学习，其中任务跨越粗粒度任务（如快速场景分类）和细粒度任务（如3D关节姿态估计）。通过使用单一层次结构解决多个细粒度任务，大大降低了对内存和速度的资源需求，这对于嵌入式视觉感知应用（如自主机器人和车辆）非常重要。

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.