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pro vyhledávání: '"Vora, Aditya"'
We present a volume rendering-based neural surface reconstruction method that takes as few as three disparate RGB images as input. Our key idea is to regularize the reconstruction, which is severely ill-posed and leaving significant gaps between the
Externí odkaz:
http://arxiv.org/abs/2306.04699
Autor:
Vora, Aditya.
Publikováno v:
Connect to the thesis.
Thesis (B.A.)--Haverford College, Dept. of Psychology, 2008.
Includes bibliographical references.
Includes bibliographical references.
Externí odkaz:
http://hdl.handle.net/10066/1329
Akademický článek
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Autor:
Vora, Aditya, Chilaka, Vinay
In this paper, we propose FCHD-Fully Convolutional Head Detector, an end-to-end trainable head detection model. Our proposed architecture is a single fully convolutional network which is responsible for both bounding box prediction and classification
Externí odkaz:
http://arxiv.org/abs/1809.08766
Autor:
Vora, Aditya
In this paper, we present a technique for unsupervised learning of visual representations. Specifically, we train a model for foreground and background classification task, in the process of which it learns visual representations. Foreground and back
Externí odkaz:
http://arxiv.org/abs/1806.00428
Autor:
Vora, Aditya, Raman, Shanmuganathan
This paper addresses the problem of unsupervised object localization in an image. Unlike previous supervised and weakly supervised algorithms that require bounding box or image level annotations for training classifiers in order to learn features rep
Externí odkaz:
http://arxiv.org/abs/1706.09719
Autor:
Vora, Aditya, Raman, Shanmuganathan
Segmenting foreground object from a video is a challenging task because of the large deformations of the objects, occlusions, and background clutter. In this paper, we propose a frame-by-frame but computationally efficient approach for video object s
Externí odkaz:
http://arxiv.org/abs/1706.09544
Autor:
Jani, Mayank H., Vora, Aditya M.
Publikováno v:
Molecular Physics; Sep2024, Vol. 122 Issue 18, p1-19, 19p
Publikováno v:
In Materials Today: Proceedings 2022 57 Part 1:275-278
Akademický článek
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