Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Michael Greenspan"'
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 10, Pp 240-256 (2022)
AbstractWe introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspective revision algorithm m
Externí odkaz:
https://doaj.org/article/ac15d1b7cf384e9ab932270c1048bb2b
Publikováno v:
Can Urol Assoc J
Traumatic Foley catheterization and malplacement is a common urological consultation. Although most urethral injuries are self-resolving, there are reports of long-standing complications, including urethral strictures, false passage formation, fistul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22ff5e96f014d0cbff9d45c93367c038
https://europepmc.org/articles/PMC9665317/
https://europepmc.org/articles/PMC9665317/
Point Cloud Registration Using Virtual Interest Points from Macaulay’s Resultant of Quadric Surfaces
Publikováno v:
Journal of Mathematical Imaging and Vision. 63:457-471
A novel formulation called Virtual Interest Point is presented and used to register point clouds. An implicit quadric surface representation is first used to model the point cloud segments. Macaulay’s resultant then provides the intersection of thr
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200793
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f48507f2cb6d4983f84e561ebf65bd8
https://doi.org/10.1007/978-3-031-20080-9_20
https://doi.org/10.1007/978-3-031-20080-9_20
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
We present the Teacher-Student Generative Adversarial Network (TS-GAN) to generate depth images from single RGB images in order to boost the performance of face recognition systems. For our method to generalize well across unseen datasets, we design
This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ad6a764669208ff4ac74bd360355b58
http://arxiv.org/abs/2106.06684
http://arxiv.org/abs/2106.06684
Autor:
Wenhan Xiong, Yufei Feng, Michael Greenspan, Xiaoxiao Guo, Junjie Huang, Shiyu Chang, Mo Yu, Xiaodan Zhu, Murray Campbell
Publikováno v:
Proceedings of the Canadian Conference on Artificial Intelligence.
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and
Publikováno v:
Proceedings of the Canadian Conference on Artificial Intelligence.
Unsupervised pretraining has recently achieved significant success on a wide variety of natural language processing tasks. An important problem that remains understudied is how to effectively adapt such models to specific domains, which is critical f
A novel object detection method is presented that handles freely rotated objects of arbitrary sizes, including tiny objects as small as $2\times 2$ pixels. Such tiny objects appear frequently in remotely sensed images, and present a challenge to rece
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5ff1d0f9aae4e6d82b91ac46cfce9d4
http://arxiv.org/abs/2104.11854
http://arxiv.org/abs/2104.11854
Autor:
Michael Greenspan, Ghani O. Lawal
Publikováno v:
VISIGRAPP (5: VISAPP)
A novel user friendly method is proposed for calibrating a procam system from a single pose of a planar chessboard target. The user simply needs to orient the chessboard in a single appropriate pose. A sequence of Gray Code patterns are projected ont
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0235e4a9ed34a1e97915b75302f1b022
http://arxiv.org/abs/2102.11395
http://arxiv.org/abs/2102.11395