Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Vinod K Kurmi"'
Autor:
Amrit Kumar Agrawal, K.V. Arya, Vikas Baghel, Abul Bashar, Munish Bhardwaj, Gaurav Bhatnagar, Cai Yee Chang, Shubhojeet Chatterjee, Antitza Dantcheva, Manoj Diwakar, Srishty Dwivedi, Anna Jia Gander, Manish Gaur, Nidhi Goel, Lalita Gupta, Feiran Huang, Weichang Huang, Ankush Jain, Indu Joshi, Prem Kumar Kalra, Vineet Kansal, Nafis Uddin Khan, Riya Kothari, Pardeep Kumar, Vinod K. Kurmi, Khin Wee Lai, Zhiying Li, Wenxiao Liu, Wei Kit Loo, Zhihan Lv, Monika Mathur, P.V.S.S.R. Chandra Mouli, Surendra Nagar, Shyam Singh Rajput, Ciro R. Rodriguez, Sumantra Dutta Roy, Sima Sahu, Abhishek Singh, Amit Kumar Singh, Prabhishek Singh, Pramod Kumar Singh, Rini Smita Thakur, Anurag Singh Tomar, Ayush Utkarsh, Santosh Kumar Vishwakarma, Haoxiang Wang, Jingyi Wu, Shuxuan Xie, Dilip Kumar Yadav, Gaurav Yadav, Ram Narayan Yadav
Publikováno v:
Digital Image Enhancement and Reconstruction ISBN: 9780323983709
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44dc43ea4b847ab42913dfd7ebce1a32
https://doi.org/10.1016/b978-0-32-398370-9.00005-6
https://doi.org/10.1016/b978-0-32-398370-9.00005-6
Autor:
Indu Joshi, Ayush Utkarsh, Riya Kothari, Vinod K. Kurmi, Antitza Dantcheva, Sumantra Dutta Roy, Prem Kumar Kalra
Publikováno v:
Digital Image Enhancement and Reconstruction ISBN: 9780323983709
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18a65a2bc53fa1f9359153041d5ac738
https://doi.org/10.1016/b978-0-32-398370-9.00009-3
https://doi.org/10.1016/b978-0-32-398370-9.00009-3
Publikováno v:
Neurocomputing. 457:168-181
Adaptation of a classifier to new domains is one of the challenging problems in machine learning. This has been addressed using many deep and non-deep learning based methods. Among the methodologies used, that of adversarial learning is widely applie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e2bd1173804d5c787038cf7d0d1c48c
https://doi.org/10.1016/j.neucom.2021.06.043
https://doi.org/10.1016/j.neucom.2021.06.043
Publikováno v:
Neurocomputing. 420:149-161
In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple meth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a5e2d0c8b3e60b0c8d010f7b73c35ed
https://doi.org/10.1016/j.neucom.2020.08.022
https://doi.org/10.1016/j.neucom.2020.08.022
Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e8dd78bb853fc7dfa96d5de093e2752
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::707e25ba4e805d288610bb9a3d475736
https://doi.org/10.1109/wacv51458.2022.00037
https://doi.org/10.1109/wacv51458.2022.00037
Autor:
Indu Joshi, Ayush Utkarsh, Riya Kothari, Sumantra Dutta Roy, Prem Kalra, Antitza Dantcheva, Vinod K Kurmi
Publikováno v:
IJCNN 2021-International Joint Conference on Neural Networks
IJCNN 2021-International Joint Conference on Neural Networks, Jul 2021, VIRTUAL, China. ⟨10.1109/IJCNN52387.2021.9533712⟩
IJCNN
IJCNN 2021-International Joint Conference on Neural Networks, Jul 2021, VIRTUAL, China. ⟨10.1109/IJCNN52387.2021.9533712⟩
IJCNN
A fingerprint region of interest (roi) segmentation algorithm is designed to separate the foreground fingerprint from the background noise. All the learning based state-of-the-art fingerprint roi segmentation algorithms proposed in the literature are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6b9e50220f64f40e8a4c7b62e2941a2
https://hal.science/hal-03524651/document
https://hal.science/hal-03524651/document
Autor:
Ayush Utkarsh, Prem Kalra, Riya Kothari, Vinod K Kurmi, Antitza Dantcheva, Indu Joshi, Sumantra Dutta Roy
Publikováno v:
IJCNN 2021-International Joint Conference on Neural Networks
IJCNN 2021-International Joint Conference on Neural Networks, Jul 2021, Shenzhen (online), China. ⟨10.1109/IJCNN52387.2021.9533528⟩
IJCNN
IJCNN 2021-International Joint Conference on Neural Networks, Jul 2021, Shenzhen (online), China. ⟨10.1109/IJCNN52387.2021.9533528⟩
IJCNN
The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back. However, the performance of standard fingerprint matching systems on noisy and poor quality fingerprints is far from satisfactory. To
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37d7eeada2571d13d54de1a380ef1e50
https://hal.science/hal-03524646
https://hal.science/hal-03524646
Autor:
Vinay P. Namboodiri, K. S. Venkatesh, Vinod K Kurmi, Preethi Jyothi, Badri N. Patro, Vipul Bajaj
Publikováno v:
ICASSP
There have been a number of techniques that have demonstrated the generation of multimedia data for one modality at a time using GANs, such as the ability to generate images, videos, and audio. However, so far, the task of multi-modal generation of d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c2b9f7f49d085ef6e265ecb2632926e
https://doi.org/10.1109/icassp39728.2021.9413802
https://doi.org/10.1109/icassp39728.2021.9413802
Publikováno v:
WACV
Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled target set, assuming that the source dataset is available with all labels. However, the availability of actual source samples is not always possible in practical cas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e850e9d8a2a56bf093769c0faf83d14
http://arxiv.org/abs/2102.09003
http://arxiv.org/abs/2102.09003