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20172023
most citedTransfer learning from synthetic to real images using variational autoencoders for robotic applications

14 citations · 42 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.CV2020

Image inpainting using frequency domain priors

Hiya Roy, Subhajit Chaudhury, Toshihiko Yamasaki +1

In this paper, we present a novel image inpainting technique using frequency domain information. Prior works on image inpainting predict the missing pixels by training neural netwo…

cs.CV20204 cited

Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos

Subhajit Chaudhury, Daiki Kimura, Phongtharin Vinayavekhin +7

Image-based sports analytics enable automatic retrieval of key events in a game to speed up the analytics process for human experts. However, most existing methods focus on structu…

cs.CV2020

Assessing Robustness of Deep learning Methods in Dermatological Workflow

Sourav Mishra, Subhajit Chaudhury, Hideaki Imaizumi +1

This paper aims to evaluate the suitability of current deep learning methods for clinical workflow especially by focusing on dermatology. Although deep learning methods have been a…

cs.CV20198 cited

Lunar surface image restoration using U-net based deep neural networks

Hiya Roy, Subhajit Chaudhury, Toshihiko Yamasaki +3

Image restoration is a technique that reconstructs a feasible estimate of the original image from the noisy observation. In this paper, we present a U-Net based deep neural network…

cs.CV2018

Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection

Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris +1

Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale imag…

cs.CV2018

Focusing on What is Relevant: Time-Series Learning and Understanding using Attention

Phongtharin Vinayavekhin, Subhajit Chaudhury, Asim Munawar +4

This paper is a contribution towards interpretability of the deep learning models in different applications of time-series. We propose a temporal attention layer that is capable of…