no code implementations • 7 Jun 2021 • Sarah Shiraz, Krishna Regmi, Shruti Vyas, Yogesh S. Rawat, Mubarak Shah
We address the problem of novel view video prediction; given a set of input video clips from a single/multiple views, our network is able to predict the video from a novel view.
1 code implementation • ICCV 2021 • Krishna Regmi, Mubarak Shah
In this paper, we address the problem of video geo-localization by proposing a Geo-Temporal Feature Learning (GTFL) Network to simultaneously learn the discriminative features between the query videos and gallery images for estimating the geo-spatial trajectory of a query video.
1 code implementation • ICCV 2019 • Krishna Regmi, Mubarak Shah
Our Feature Fusion method combines the complementary features from a synthesized aerial image with the corresponding ground features to obtain a robust query representation.
1 code implementation • 1 Dec 2018 • Mohamed Elfeki, Krishna Regmi, Shervin Ardeshir, Ali Borji
In this work, we introduce two datasets (synthetic and natural/real) containing simultaneously recorded egocentric and exocentric videos.
2 code implementations • 14 Aug 2018 • Krishna Regmi, Ali Borji
For this, we propose to use homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image.
1 code implementation • CVPR 2018 • Krishna Regmi, Ali Borji
X-Fork architecture has a single discriminator and a single generator.
no code implementations • 17 Dec 2016 • Shervin Ardeshir, Krishna Regmi, Ali Borji
On one hand, the abundance of egocentric cameras in the past few years has offered the opportunity to study a lot of vision problems from the first-person perspective.