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20162021
most citedGenerating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction

14 citations · 20 across the 2 of their papers we have counts for

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9 papers · 1 filter

cs.CV202114 cited

Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction

Stefan Becker, Ronny Hug, Wolfgang Hübner +2

Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are incr…

cs.CV2021

Handling Missing Observations with an RNN-based Prediction-Update Cycle

Stefan Becker, Ronny Hug, Wolfgang Hübner +2

In tasks such as tracking, time-series data inevitably carry missing observations. While traditional tracking approaches can handle missing observations, recurrent neural networks…

cs.CV2021

Win-Fail Action Recognition

Paritosh Parmar, Brendan Morris

Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize…

cs.CV2021

Piano Skills Assessment

Paritosh Parmar, Jaiden Reddy, Brendan Morris

Can a computer determine a piano player's skill level? Is it preferable to base this assessment on visual analysis of the player's performance or should we trust our ears over our…

cs.CV2019

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

Paritosh Parmar, Brendan Morris

Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action related tasks. However, 3D-CNN are…

cs.CV20196 cited

What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

Paritosh Parmar, Brendan Tran Morris

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches p…