Bat-G2 Net: Bat-Inspired Graphical Visualization Network Guided by Radiated Ultrasonic Call

Autor: Seohyeon Kim, Gunpil Hwang, Hyeon-Min Bae

Publikováno v: IEEE Access, Vol 8, Pp 189673-189683 (2020)

In this paper, a noise-immune Bat-inspired Graphical visualization network Guided by the radiated ultrasonic call (Bat-G2 net) that can reconstruct 3D shapes of a target from ultrasonic echoes is presented. The Bat-G2 net achieves noise-resiliency by

Externí odkaz: https://doaj.org/article/e52a715161a24390a9f7e96816d8d700

DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders

Autor: Garau, Nicola, Bisagno, Niccolò, Brodka, Piotr, Conci, Nicola

Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos. We show that current 3D HPE methods suffer a lack of viewpoint equivariance, namely they tend to fail or perform poorly when dealing with viewpoints

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b63ef7b2b93185a7bb2e474bd3ad2fc
http://arxiv.org/abs/2108.08557

Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

Autor: Nicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah

Publikováno v: Ristea, N, Madan, N, Ionescu, R T, Nasrollahi, K, Shahbaz Khan, F, Moeslund, T B & shah, M 2022, Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection . in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, I E E E Conference on Computer Vision and Pattern Recognition. Proceedings, pp. 13566-13576, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, United States, 18/06/2022 . https://doi.org/10.1109/CVPR52688.2022.01321

Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detecti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10742e9669843938682341a528b99960
http://arxiv.org/abs/2111.09099

Privacy protection in street-view panoramas using depth and multi-view imagery

Autor: Ries Uittenbogaard, Clint Sebastian, Julien Vijverberg, Bas Boom, Dariu M. Gavrila, Peter H.N. de With

Publikováno v: 13th IEEE/CVF Conference on Computer Vision and Pattern Recognition, (CVPR2019), 10573-10582
STARTPAGE=10573;ENDPAGE=10582;TITLE=13th IEEE/CVF Conference on Computer Vision and Pattern Recognition, (CVPR2019)
Proceedings IEEE Computer Vision and Pattern Recognition (CVPR 2019)
CVPR

The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pede

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fee20c6bc258edb3fb0b4518c6b127de
https://research.tue.nl/nl/publications/6f89a162-f988-4636-993e-80ca7a2eebbc

Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation

Autor: Rita Cucchiara, Lorenzo Baraldi, Matteo Tomei, Marcella Cornia

Publikováno v: CVPR

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed0b6f3173d5f62eee2b891832f33231
http://arxiv.org/abs/1811.10666