Zobrazeno 1 - 10
of 5 270
pro vyhledávání: '"Pseudo-labeling"'
Publikováno v:
MICCAI Workshop on Deep Generative Models, pp. 54-63. Cham: Springer Nature Switzerland, 2024
The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to add
Externí odkaz:
http://arxiv.org/abs/2412.02858
Video captioning generate a sentence that describes the video content. Existing methods always require a number of captions (\eg, 10 or 20) per video to train the model, which is quite costly. In this work, we explore the possibility of using only on
Externí odkaz:
http://arxiv.org/abs/2411.04059
Autor:
Mozafari, Mohammad, Hasani, Hosein, Vahidimajd, Reza, Fereydooni, Mohamadreza, Baghshah, Mahdieh Soleymani
In recent years, few-shot segmentation (FSS) models have emerged as a promising approach in medical imaging analysis, offering remarkable adaptability to segment novel classes with limited annotated data. Existing approaches to few-shot segmentation
Externí odkaz:
http://arxiv.org/abs/2410.09967
Autor:
Liu, Shiya1 (AUTHOR) liushiya@fosu.edu.cn, Zhu, Zheshuai1 (AUTHOR) 2112052078@stu.fosu.edu.cn, Chen, Zibin1 (AUTHOR) 2112202070@stu.fosu.edu.cn, He, Jun1 (AUTHOR) 2112202075@stu.fosu.edu.cn, Chen, Xingda1 (AUTHOR), Chen, Zhiwen2 (AUTHOR) zhiwen.chen@csu.edu.cn
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 21, p6907. 16p.
As small unmanned aerial vehicles (UAVs) become increasingly prevalent, there is growing concern regarding their impact on public safety and privacy, highlighting the need for advanced tracking and trajectory estimation solutions. In response, this p
Externí odkaz:
http://arxiv.org/abs/2412.12698
Autor:
Salmanpour, Mohammad R., Gorji, Arman, Mousavi, Amin, Jouzdani, Ali Fathi, Sanati, Nima, Maghsudi, Mehdi, Leung, Bonnie, Ho, Cheryl, Yuan, Ren, Rahmim, Arman
Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybri
Externí odkaz:
http://arxiv.org/abs/2412.00068
The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust c
Externí odkaz:
http://arxiv.org/abs/2410.14686
Autor:
Phan, Han1 (AUTHOR), Brouard, Céline1 (AUTHOR), Mourad, Raphaël1,2 (AUTHOR) raphael.mourad@univ-tlse3.fr
Publikováno v:
Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-9. 9p.
Autor:
Bhogale, Kaushal Santosh, Mehendale, Deovrat, Parasa, Niharika, G, Sathish Kumar Reddy, Javed, Tahir, Kumar, Pratyush, Khapra, Mitesh M.
In this study, we tackle the challenge of limited labeled data for low-resource languages in ASR, focusing on Hindi. Specifically, we explore pseudo-labeling, by proposing a generic framework combining multiple ideas from existing works. Our framewor
Externí odkaz:
http://arxiv.org/abs/2408.14026
There is a growing demand in the field of KIE (Key Information Extraction) to apply semi-supervised learning to save manpower and costs, as training document data using fully-supervised methods requires labor-intensive manual annotation. The main cha
Externí odkaz:
http://arxiv.org/abs/2407.15873