Zobrazeno 1 - 10
of 1 247
pro vyhledávání: '"Radeva, Petia"'
Food segmentation, including in videos, is vital for addressing real-world health, agriculture, and food biotechnology issues. Current limitations lead to inaccurate nutritional analysis, inefficient crop management, and suboptimal food processing, i
Externí odkaz:
http://arxiv.org/abs/2407.12121
Autor:
He, Jiangpeng, Chen, Yuhao, Vinod, Gautham, Mahmud, Talha Ibn, Zhu, Fengqing, Delp, Edward, Wong, Alexander, Xi, Pengcheng, AlMughrabi, Ahmad, Haroon, Umair, Marques, Ricardo, Radeva, Petia, Tang, Jiadong, Yang, Dianyi, Gao, Yu, Liang, Zhaoxiang, Jueluo, Yawei, Shi, Chengyu, Wang, Pengyu
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. However, the scarcity of high-quality data and limited collaboration betw
Externí odkaz:
http://arxiv.org/abs/2407.09285
Autor:
Rodríguez-de-Vera, Jesús M, Estepa, Imanol G, Sarasúa, Ignacio, Nagarajan, Bhalaji, Radeva, Petia
In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretraining robust backbones. In this paper, we challenge this idea by exploring if it is possible to bridg
Externí odkaz:
http://arxiv.org/abs/2407.03463
We propose MomentsNeRF, a novel framework for one- and few-shot neural rendering that predicts a neural representation of a 3D scene using Orthogonal Moments. Our architecture offers a new transfer learning method to train on multi-scenes and incorpo
Externí odkaz:
http://arxiv.org/abs/2407.02668
Accurate food volume estimation is essential for dietary assessment, nutritional tracking, and portion control applications. We present VolETA, a sophisticated methodology for estimating food volume using 3D generative techniques. Our approach create
Externí odkaz:
http://arxiv.org/abs/2407.01717
Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. While traditional Multi-View Stereo (MVS) systems have been fundamental in these ap
Externí odkaz:
http://arxiv.org/abs/2406.13515
Medical image fusion integrates the complementary diagnostic information of the source image modalities for improved visualization and analysis of underlying anomalies. Recently, deep learning-based models have excelled the conventional fusion method
Externí odkaz:
http://arxiv.org/abs/2310.11910
Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients' disorders and provide guidance during preoperative and intra-operative procedures. Deep le
Externí odkaz:
http://arxiv.org/abs/2310.11896
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD)
Externí odkaz:
http://arxiv.org/abs/2309.02995
Autor:
Salih, Ahmed, Raisi-Estabragh, Zahra, Galazzo, Ilaria Boscolo, Radeva, Petia, Petersen, Steffen E., Menegaz, Gloria, Lekadir, Karim
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transpare
Externí odkaz:
http://arxiv.org/abs/2305.02012