Machine Learning & Data Analytics

In this paper, we develop a new model for recognizing human actions.

In this paper, we propose a structured kernel machine approach to treat object detection and pose estimation jointly in a mutually beneficial way.

We propose a hierarchical generalization of the ddCRP which clusters data within groups based on distances between data items, and couples clusters across groups via distances based on aggregate properties of these local clusters.

We propose a new weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video.

We present a hierarchical model for human activity recognition in entire multi-person scenes.

Automatic recovery of 3d pose of multiple interacting subjects from unconstrained monocular image sequence is a challenging and largely unaddressed problem.

We present a type of Temporal Restricted Boltzmann Machine that defines a probability distribution over an output sequence conditional on an input sequence.

This work reports the first experimental demonstration of CIT from a vertical magnetic dipole (VMD) in remote sensing and position tracking applications.

Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game.

We describe the conceptual design, architecture, and implementation of a multimodal, robot-child dialogue system in a fast-paced, speech-controlled collaborative game.