Harit Pandya

TL;DR: This paper presents an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori.

TL;DR: This work proposes a new deep approach to Kalman filtering which can be learned directly in an end-to-end manner using backpropagation without additional approximations and uses a high-dimensional factorized latent state representation for which the Kalman updates simplify to scalar operations and thus avoids hard to backpropagate, computationally heavy and potentially unstable matrix inversions.

TL;DR: This work attempts the raga classification problem in a non-linear SVM framework using a combination of two kernels that represent the similarities of a music signal using two different features-pitch-class profile and n-gram distribution of notes.

TL;DR: This paper considers a novel problem of servoing any instance (exemplar) of an object category to a desired pose (view) and proposes a strategy to accomplish the task and shows the performance gain achieved by using a linear combination of models (instances) vis a vis a controller that switches across models during servoing.

TL;DR: This paper presents a framework for visual servoing to a novel object instance, capable of autonomously navigating to every essential part of a vehicle for inspection and can be initialized from any random pose, and is the first approach demonstrating fully autonomous visual inspection of vehicles using MAVs.