Zobrazeno 1 - 10
of 221
pro vyhledávání: '"Castellano Giovanna"'
Autor:
Casalino Gabriella, Castellano Giovanna, Hryniewicz Olgierd, Leite Daniel, Opara Karol, Radziszewska Weronika, Kaczmarek-Majer Katarzyna
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 33, Iss 3, Pp 419-428 (2023)
Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients. Nonetheless, the psychiatric assessment of the
Externí odkaz:
https://doaj.org/article/dd165620b0dc4088b85d7975b2940d8c
Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep learning
Externí odkaz:
http://arxiv.org/abs/2410.04983
Artificial Intelligence and generative models have revolutionized music creation, with many models leveraging textual or visual prompts for guidance. However, existing image-to-music models are limited to simple images, lacking the capability to gene
Externí odkaz:
http://arxiv.org/abs/2410.04906
Autor:
De Marinis, Pasquale, Fanelli, Nicola, Scaringi, Raffaele, Colonna, Emanuele, Fiameni, Giuseppe, Vessio, Gennaro, Castellano, Giovanna
We present Label Anything, an innovative neural network architecture designed for few-shot semantic segmentation (FSS) that demonstrates remarkable generalizability across multiple classes with minimal examples required per class. Diverging from trad
Externí odkaz:
http://arxiv.org/abs/2407.02075
Publikováno v:
Neurocomputing (2023)
Crowd analysis from drones has attracted increasing attention in recent times due to the ease of use and affordable cost of these devices. However, how this technology can provide a solution to crowd flow detection is still an unexplored research que
Externí odkaz:
http://arxiv.org/abs/2301.04937
Autor:
Du, Dawei, Wen, Longyin, Zhu, Pengfei, Fan, Heng, Hu, Qinghua, Ling, Haibin, Shah, Mubarak, Pan, Junwen, Al-Ali, Ali, Mohamed, Amr, Imene, Bakour, Dong, Bin, Zhang, Binyu, Nesma, Bouchali Hadia, Xu, Chenfeng, Duan, Chenzhen, Castiello, Ciro, Mencar, Corrado, Liang, Dingkang, Krüger, Florian, Vessio, Gennaro, Castellano, Giovanna, Wang, Jieru, Gao, Junyu, Abualsaud, Khalid, Ding, Laihui, Zhao, Lei, Cianciotta, Marco, Saqib, Muhammad, Almaadeed, Noor, Elharrouss, Omar, Lyu, Pei, Wang, Qi, Liu, Shidong, Qiu, Shuang, Pan, Siyang, Al-Maadeed, Somaya, Khan, Sultan Daud, Khattab, Tamer, Han, Tao, Golda, Thomas, Xu, Wei, Bai, Xiang, Xu, Xiaoqing, Li, Xuelong, Zhao, Yanyun, Tian, Ye, Lin, Yingnan, Xu, Yongchao, Yao, Yuehan, Xu, Zhenyu, Zhao, Zhijian, Luo, Zhipeng, Wei, Zhiwei, Zhao, Zhiyuan
Publikováno v:
European Conference on Computer Vision. Springer, Cham, 2020: 675-691
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the dr
Externí odkaz:
http://arxiv.org/abs/2107.08766
Autor:
Castellano, Giovanna, Vessio, Gennaro
Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns based on domain knowledge and visual perception is extremely hard. On the other hand, applying traditional clustering and feature reduction techniq
Externí odkaz:
http://arxiv.org/abs/2106.06234
Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community. However, most of the current work is mainly based solely on digitized artwork images, sometimes supplemented with some metadata an
Externí odkaz:
http://arxiv.org/abs/2105.15028
Autor:
Castellano, Giovanna1 (AUTHOR), Esposito, Andrea1 (AUTHOR), Lella, Eufemia2 (AUTHOR), Montanaro, Graziano3 (AUTHOR), Vessio, Gennaro1 (AUTHOR) gennaro.vessio@uniba.it
Publikováno v:
Scientific Reports. 3/4/2024, Vol. 14 Issue 1, p1-10. 10p.
Autor:
Castellano, Giovanna, Vessio, Gennaro
Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns in accordance with domain knowledge and visual perception is extremely difficult. On the other hand, applying traditional clustering and feature re
Externí odkaz:
http://arxiv.org/abs/2003.08597