Title: 3D Object Detection from CT Scans using a Slice-and-fuse Approach   Abstract: Automatic object detection in 3D X-ray Computed Tomography imagery has recently gained research attention due to its promising applications in aviation baggage screening. The huge dimension of an individual 3D scan, however, poses formidable computational challenges when coupled with deep 3D convolutional [...]

Title: Interpretable Intuitive Physics Model   Abstract: Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes [...]

Title: Learning for Registration in 2D and 3D   Abstract: We explore the application of deep learning to 2D (image) and 3D (point cloud) registration, especially in scenarios where traditional methods can fail.   In the 2D case, we apply a recently-proposed learning method to the problem of aligning outdoor imagery taken across different seasons or [...]

Title: Inverse Reinforcement Learning for Autonomous Ground Navigation Using Aerial and Satellite Observation Data   Abstract: Inverse Reinforcement Learning(IRL) is a supervised learning paradigm where a learner observes expert demonstrations to learn the hidden cost function to mimic the expert's behavior. Eliminating the need for elaborate feature engineering, deep IRL approaches have been gaining interests [...]

Title: Detecting objects in videos under occlusion   Abstract: While object detection in videos has received a lot of attention in the past few years, most existing methods in this domain do not target detecting objects when they are occluded. However, being able to detect or track an object of interest through occlusion has been [...]

Title: Detailed Image Captioning with Hierarchical Attention   Abstract:  Automatic image description is the task of generating a natural sentence which reflects the visual content of an image. A lot of deep learning architectures have been explored in the past few years. While researchers have made great improvement on generating syntactically correct sentences by learning from [...]

Title:  Inching for Planetary Rovers   Abstract: . Inching, also called push-rolling, is method of moving for vehicles that can change the position of their wheels relative to their body.  Like an inchworm, it is possible to hold some wheels stationary while advancing the others and the body.  In this research a test apparatus capable [...]

Title: Improving Scalability in Multi-Robot Systems with Abstraction and Specialization   Abstract: Planning and controlling multi-robot systems is challenging due to high dimensionality. When the number of robots increases in the system, the complexity of computation grows exponentially. In this thesis, we examine the scalability problem in planning and control of the multi-robot system. We propose [...]

Title: A Generative Approach for Socially Compliant Navigation   Abstract: Robots navigating in human crowds need to optimize their paths not only for the efficiency of their tasks performance but also for the compliance to social norms. One of the key challenges in this context is due to the lack of suitable metrics for evaluating and [...]

Title: 3D Face Geometry Capture Using Monocular Video   Abstract: Accurate reconstruction of facial geometry has been one of the oldest tasks in computer vision. Despite being a long-studied problem, many modern methods fail to reconstruct realistic looking faces or rely on highly constrained environments for capture. High fidelity face reconstructions have so far been [...]

Title: Unsupervised Learning for 3D Reconstruction and Blocks World Representation Abstract: Recovering the dense 3D structure of a scene from its images has been a long-standing goal in computer vision. Recent years have seen attempts of encoding richer priors into the geometry-based pipelines with the introduction of learning based methods. We argue that the form of 3D [...]

Title: A structured model for action detection   Abstract:  A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand. While this is an obviously attractive approach, [...]

Title: Canonical Surface Mapping via Geometric Cycle Consistency   Abstract: We explore the task of Canonical Surface Mapping (CSM).  Specifically, given an image, we learn to map pixels on the object to their corresponding locations on an abstract 3D model of the category. But how do we learn such a mapping? A supervised approach would [...]