MSR Thesis Defense
Carnegie Mellon University
Learning with Auxiliary Supervision
Abstract: Supervised learning for high-level vision tasks has advanced significantly over the last decade. One of the primary driving forces for these improvements has been the availability of vast amounts of labeled data. However, annotating data is an expensive and time-consuming process. For example, densely segmenting a natural scene image takes approximately 30 minutes. This mode [...]
Inverse Reinforcement Learning with Conditional Choice Probabilities
Abstract: We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm to solve the IRL problem, using easy-to-compute estimates of the Conditional Choice Probability (CCP) vector, which is the policy function of an expert integrated over factors econometricians cannot [...]
Monocular Depth Reconstruction using Geometry and Deep Networks
In this thesis, we explore methods of building dense depth map from monocular video. First, we introduce our multi-view stereo pipeline, which utilizes photometric bundle adjustment for getting accurate depth of textured regions from small motion video. Second, we improve the depth estimation of low-texture region by fusing deep convolutional network predictions. We categorize the [...]
Carnegie Mellon University
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo. In previous works, separate pose and [...]
Carnegie Mellon University
Learning-based Lane Following and Changing Behaviors for Autonomous Vehicle
This thesis explores learning-based methods in generating human-like lane following and changing behaviors in on-road autonomous driving. We summarize our main contributions as: 1) derive an efficient vision-based end-to-end learning system for on-road driving; 2) propose a novel attention-based learning architecture with sub-action space to obtain lane changing behavior using a deep reinforcement learning algorithm; [...]