5 papers
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling
Zongyao Lyu, William J. Beksi
Active learning aims to alleviate the amount of labor involved in data labeling by automating the selection of unlabeled samples via an acquisition function. For example, variation…
A Human-Centered Approach for Bootstrapping Causal Graph Creation
Minh Q. Tram, Nolan B. Gutierrez, William J. Beksi
Causal inference, a cornerstone in disciplines such as economics, genomics, and medicine, is increasingly being recognized as fundamental to advancing the field of robotics. In par…
IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D Reconstruction
Mohammad Samiul Arshad, William J. Beksi
Reconstruction of 3D open surfaces (e.g., non-watertight meshes) is an underexplored area of computer vision. Recent learning-based implicit techniques have removed previous barrie…
LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction
Mohammad Samiul Arshad, William J. Beksi
Accurate reconstruction of both the geometric and topological details of a 3D object from a single 2D image embodies a fundamental challenge in computer vision. Existing explicit/i…
Intuitive Robot Integration via Virtual Reality Workspaces
Minh Q. Tram, Joseph M. Cloud, William J. Beksi
As robots become increasingly prominent in diverse industrial settings, the desire for an accessible and reliable system has correspondingly increased. Yet, the task of meaningfull…