Zobrazeno 1 - 10
of 4 069
pro vyhledávání: '"Newcombe P"'
Autor:
Hong, Fangzhou, Guzov, Vladimir, Kim, Hyo Jin, Ye, Yuting, Newcombe, Richard, Liu, Ziwei, Ma, Lingni
As the prevalence of wearable devices, learning egocentric motions becomes essential to develop contextual AI. In this work, we present EgoLM, a versatile framework that tracks and understands egocentric motions from multi-modal inputs, e.g., egocent
Externí odkaz:
http://arxiv.org/abs/2409.18127
Autor:
Guzov, Vladimir, Jiang, Yifeng, Hong, Fangzhou, Pons-Moll, Gerard, Newcombe, Richard, Liu, C. Karen, Ye, Yuting, Ma, Lingni
This paper investigates the online generation of realistic full-body human motion using a single head-mounted device with an outward-facing color camera and the ability to perform visual SLAM. Given the inherent ambiguity of this setup, we introduce
Externí odkaz:
http://arxiv.org/abs/2409.13426
Autor:
Asiri, Zayed, Burdett, Ryan, Chimani, Markus, Haythorpe, Michael, Newcombe, Alex, Wagner, Mirko H.
Determining the crossing numbers of Cartesian products of small graphs with arbitrarily large paths has been an ongoing topic of research since the 1970s. Doing so requires the establishment of coincident upper and lower bounds; the former is usually
Externí odkaz:
http://arxiv.org/abs/2409.06755
Autor:
Jiang, Shuaifeng, Qu, Qi, Pan, Xiaqing, Agrawal, Abhishek, Newcombe, Richard, Alkhateeb, Ahmed
Fully harvesting the gain of multiple-input and multiple-output (MIMO) requires accurate channel information. However, conventional channel acquisition methods mainly rely on pilot training signals, resulting in significant training overheads (time,
Externí odkaz:
http://arxiv.org/abs/2409.02564
Autor:
Wagner, Felix, Xu, Wentian, Saha, Pramit, Liang, Ziyun, Whitehouse, Daniel, Menon, David, Newcombe, Virginia, Voets, Natalie, Noble, J. Alison, Kamnitsas, Konstantinos
Segmentation models for brain lesions in MRI are commonly developed for a specific disease and trained on data with a predefined set of MRI modalities. Each such model cannot segment the disease using data with a different set of MRI modalities, nor
Externí odkaz:
http://arxiv.org/abs/2406.11636
The theory of products of random matrices and Lyapunov exponents have been widely studied and applied in the fields of biology, dynamical systems, economics, engineering and statistical physics. We consider the product of an i.i.d. sequence of $2\tim
Externí odkaz:
http://arxiv.org/abs/2406.10364
The advent of wearable computers enables a new source of context for AI that is embedded in egocentric sensor data. This new egocentric data comes equipped with fine-grained 3D location information and thus presents the opportunity for a novel class
Externí odkaz:
http://arxiv.org/abs/2406.10224
Autor:
Ma, Lingni, Ye, Yuting, Hong, Fangzhou, Guzov, Vladimir, Jiang, Yifeng, Postyeni, Rowan, Pesqueira, Luis, Gamino, Alexander, Baiyya, Vijay, Kim, Hyo Jin, Bailey, Kevin, Fosas, David Soriano, Liu, C. Karen, Liu, Ziwei, Engel, Jakob, De Nardi, Renzo, Newcombe, Richard
We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) full-body ground-truth motion; b) multiple multimodal egocentric data fr
Externí odkaz:
http://arxiv.org/abs/2406.09905
Autor:
Banerjee, Prithviraj, Shkodrani, Sindi, Moulon, Pierre, Hampali, Shreyas, Zhang, Fan, Fountain, Jade, Miller, Edward, Basol, Selen, Newcombe, Richard, Wang, Robert, Engel, Jakob Julian, Hodan, Tomas
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rig
Externí odkaz:
http://arxiv.org/abs/2406.09598
Autor:
Xu, Wentian, Moffat, Matthew, Seale, Thalia, Liang, Ziyun, Wagner, Felix, Whitehouse, Daniel, Menon, David, Newcombe, Virginia, Voets, Natalie, Banerjee, Abhirup, Kamnitsas, Konstantinos
Publikováno v:
Proceedings of Machine Learning Research, MIDL 2024
Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determined by a protocol for the specific disease. This work explores the follo
Externí odkaz:
http://arxiv.org/abs/2405.18511