2022
Zhu, Sijie; Shah, Mubarak; Chen, Chen
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Cross-View, CVPR, Geo-Localization, Transformers | Links:
@conference{nokey_107,
title = {TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization},
author = {Sijie Zhu and Mubarak Shah and Chen Chen},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695-supp.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Cross-View, CVPR, Geo-Localization, Transformers},
pubstate = {published},
tppubtype = {conference}
}
Zhu, Sijie; Shah, Mubarak; Chen, Chen
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Cross-View, CVPR, Geo-Localization, Transformers | Links:
@conference{nokey_155,
title = {TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization},
author = {Sijie Zhu and Mubarak Shah and Chen Chen},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695-supp.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Cross-View, CVPR, Geo-Localization, Transformers},
pubstate = {published},
tppubtype = {conference}
}
2021
Shiraz, Sarah; Regmi, Krishna; Vyas, Shruti; Rawat, Yogesh Singh; Shah, Mubarak
Novel View Video Prediction using Dual Representation Conference
IEEE International Conference on Image Processing, 2021.
Tags: Cross-View, ICIP, View Synthesis | Links:
@conference{Shiraz2021,
title = {Novel View Video Prediction using Dual Representation},
author = {Sarah Shiraz and Krishna Regmi and Shruti Vyas and Yogesh Singh Rawat and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Projects_Novel-View-Video-Prediction-using-dual-Representation.pdf
https://www.crcv.ucf.edu/research/projects/novel-view-video-prediction-using-dual-representation/},
year = {2021},
date = {2021-09-19},
urldate = {2021-09-19},
booktitle = {IEEE International Conference on Image Processing},
keywords = {Cross-View, ICIP, View Synthesis},
pubstate = {published},
tppubtype = {conference}
}
Shiraz, Sarah; Regmi, Krishna; Vyas, Shruti; Rawat, Yogesh Singh; Shah, Mubarak
Novel View Video Prediction using Dual Representation Conference
IEEE International Conference on Image Processing, 2021.
Tags: Cross-View, ICIP, View Synthesis | Links:
@conference{Shiraz2021b,
title = {Novel View Video Prediction using Dual Representation},
author = {Sarah Shiraz and Krishna Regmi and Shruti Vyas and Yogesh Singh Rawat and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Projects_Novel-View-Video-Prediction-using-dual-Representation.pdf
https://www.crcv.ucf.edu/research/projects/novel-view-video-prediction-using-dual-representation/},
year = {2021},
date = {2021-09-19},
urldate = {2021-09-19},
booktitle = {IEEE International Conference on Image Processing},
keywords = {Cross-View, ICIP, View Synthesis},
pubstate = {published},
tppubtype = {conference}
}
Regmi, Krishna
Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching PhD Thesis
University of Central Florida, 2021.
Tags: Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization | Links:
@phdthesis{Regmi2021,
title = {Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching},
author = {Krishna Regmi},
url = {https://stars.library.ucf.edu/etd2020/747/},
year = {2021},
date = {2021-08-02},
urldate = {2021-08-02},
school = {University of Central Florida},
keywords = {Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization},
pubstate = {published},
tppubtype = {phdthesis}
}
Regmi, Krishna
Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching PhD Thesis
University of Central Florida, 2021.
Tags: Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization | Links:
@phdthesis{Regmi2021b,
title = {Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching},
author = {Krishna Regmi},
url = {https://stars.library.ucf.edu/etd2020/747/},
year = {2021},
date = {2021-08-02},
urldate = {2021-08-02},
school = {University of Central Florida},
keywords = {Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization},
pubstate = {published},
tppubtype = {phdthesis}
}
2020
Schatz, Kara Marie; Quintanilla, Erik; Vyas, Shruti; Rawat, Yogesh Singh
A Recurrent Transformer Network for Novel View Action Synthesis Conference
16th European Conference on Computer Vision, 2020.
Abstract | Tags: Cross-View, ECCV, REU, View Action Synthesis, View Synthesis | Links:
@conference{Schatz2020,
title = {A Recurrent Transformer Network for Novel View Action Synthesis},
author = {Kara Marie Schatz and Erik Quintanilla and Shruti Vyas and Yogesh Singh Rawat},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/07/Projects_A-Recurrent-Transformer-Network-for-Novel-View-Action-Synthesis.pdf
https://www.crcv.ucf.edu/research/projects/a-recurrent-transformer-network-for-novel-view-action-synthesis/},
year = {2020},
date = {2020-08-23},
urldate = {2020-08-23},
booktitle = {16th European Conference on Computer Vision},
abstract = {In this work, we address the problem of synthesizing human actions from novel views. Given an input video of an actor performing some action, we aim to synthesize a video with the same action performed from a novel view with the help of an appearance prior. We propose an end-to-end deep network to solve this problem. The proposed network utilizes the change in viewpoint to transform the action from the input view to the novel view in feature space. The transformed action is integrated with the target appearance using the proposed recurrent transformer network, which provides a transformed appearance for each time-step in the action sequence. The recurrent transformer network utilize action key-points which are determined in an unsupervised approach using the encoded action features. We also propose a hierarchical structure for the recurrent transformation which further improves the performance. We demonstrate the effectiveness of the proposed method through extensive experiments conducted on a large-scale multi-view action recognition NTU-RGB+D dataset. In addition, we show that the proposed method can transform the action to a novel viewpoint with an entirely different scene or actor. The code is publicly available at https://github.com/schatzkara/cross-view-video.},
keywords = {Cross-View, ECCV, REU, View Action Synthesis, View Synthesis},
pubstate = {published},
tppubtype = {conference}
}
In this work, we address the problem of synthesizing human actions from novel views. Given an input video of an actor performing some action, we aim to synthesize a video with the same action performed from a novel view with the help of an appearance prior. We propose an end-to-end deep network to solve this problem. The proposed network utilizes the change in viewpoint to transform the action from the input view to the novel view in feature space. The transformed action is integrated with the target appearance using the proposed recurrent transformer network, which provides a transformed appearance for each time-step in the action sequence. The recurrent transformer network utilize action key-points which are determined in an unsupervised approach using the encoded action features. We also propose a hierarchical structure for the recurrent transformation which further improves the performance. We demonstrate the effectiveness of the proposed method through extensive experiments conducted on a large-scale multi-view action recognition NTU-RGB+D dataset. In addition, we show that the proposed method can transform the action to a novel viewpoint with an entirely different scene or actor. The code is publicly available at https://github.com/schatzkara/cross-view-video.
Schatz, Kara Marie; Quintanilla, Erik; Vyas, Shruti; Rawat, Yogesh Singh
A Recurrent Transformer Network for Novel View Action Synthesis Conference
16th European Conference on Computer Vision, 2020.
Abstract | Tags: Cross-View, ECCV, REU, View Action Synthesis, View Synthesis | Links:
@conference{Schatz2020b,
title = {A Recurrent Transformer Network for Novel View Action Synthesis},
author = {Kara Marie Schatz and Erik Quintanilla and Shruti Vyas and Yogesh Singh Rawat},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/07/Projects_A-Recurrent-Transformer-Network-for-Novel-View-Action-Synthesis.pdf
https://www.crcv.ucf.edu/research/projects/a-recurrent-transformer-network-for-novel-view-action-synthesis/},
year = {2020},
date = {2020-08-23},
urldate = {2020-08-23},
booktitle = {16th European Conference on Computer Vision},
abstract = {In this work, we address the problem of synthesizing human actions from novel views. Given an input video of an actor performing some action, we aim to synthesize a video with the same action performed from a novel view with the help of an appearance prior. We propose an end-to-end deep network to solve this problem. The proposed network utilizes the change in viewpoint to transform the action from the input view to the novel view in feature space. The transformed action is integrated with the target appearance using the proposed recurrent transformer network, which provides a transformed appearance for each time-step in the action sequence. The recurrent transformer network utilize action key-points which are determined in an unsupervised approach using the encoded action features. We also propose a hierarchical structure for the recurrent transformation which further improves the performance. We demonstrate the effectiveness of the proposed method through extensive experiments conducted on a large-scale multi-view action recognition NTU-RGB+D dataset. In addition, we show that the proposed method can transform the action to a novel viewpoint with an entirely different scene or actor. The code is publicly available at https://github.com/schatzkara/cross-view-video.},
keywords = {Cross-View, ECCV, REU, View Action Synthesis, View Synthesis},
pubstate = {published},
tppubtype = {conference}
}
In this work, we address the problem of synthesizing human actions from novel views. Given an input video of an actor performing some action, we aim to synthesize a video with the same action performed from a novel view with the help of an appearance prior. We propose an end-to-end deep network to solve this problem. The proposed network utilizes the change in viewpoint to transform the action from the input view to the novel view in feature space. The transformed action is integrated with the target appearance using the proposed recurrent transformer network, which provides a transformed appearance for each time-step in the action sequence. The recurrent transformer network utilize action key-points which are determined in an unsupervised approach using the encoded action features. We also propose a hierarchical structure for the recurrent transformation which further improves the performance. We demonstrate the effectiveness of the proposed method through extensive experiments conducted on a large-scale multi-view action recognition NTU-RGB+D dataset. In addition, we show that the proposed method can transform the action to a novel viewpoint with an entirely different scene or actor. The code is publicly available at https://github.com/schatzkara/cross-view-video.
2004
Xiao, Jiangjian; Shah, Mubarak
Tri-view Morphing Journal Article
In: Special issue of "Computer Vision and Image Understanding", 2004.
Tags: Cross-View, Deep Learning, Rendering, View Synthesis | Links:
@article{Xiao2004c,
title = {Tri-view Morphing},
author = {Jiangjian Xiao and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/tri-view-cviu.pdf},
year = {2004},
date = {2004-01-29},
urldate = {2004-01-29},
journal = {Special issue of "Computer Vision and Image Understanding"},
keywords = {Cross-View, Deep Learning, Rendering, View Synthesis},
pubstate = {published},
tppubtype = {article}
}
Xiao, Jiangjian; Shah, Mubarak
Tri-view Morphing Journal Article
In: Special issue of "Computer Vision and Image Understanding", 2004.
Tags: Cross-View, Deep Learning, Rendering, View Synthesis | Links:
@article{Xiao2004cb,
title = {Tri-view Morphing},
author = {Jiangjian Xiao and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/tri-view-cviu.pdf},
year = {2004},
date = {2004-01-29},
urldate = {2004-01-29},
journal = {Special issue of "Computer Vision and Image Understanding"},
keywords = {Cross-View, Deep Learning, Rendering, View Synthesis},
pubstate = {published},
tppubtype = {article}
}