Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Babu Radhakrishnan"'
Autor:
Keisuke Obata, Michael Schwarze, Tabea A. Thiel, Xinyi Zhang, Babu Radhakrishnan, Ibbi Y. Ahmet, Roel van de Krol, Reinhard Schomäcker, Fatwa F. Abdi
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract With the increasing pressure to decarbonize our society, green hydrogen has been identified as a key element in a future fossil fuel-free energy infrastructure. Solar water splitting through photoelectrochemical approaches is an elegant way
Externí odkaz:
https://doaj.org/article/2329f069d51142a1b313c57e1e0b3d6c
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:2162-2170
We attempt to train deep neural networks for classification without using any labeled data. Existing unsupervised methods, though mine useful clusters or features, require some annotated samples to facilitate the final task-specific predictions. This
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc32b59862ab5c6dcdfcdb5f1ca3b703
https://doi.org/10.1609/aaai.v36i2.20113
https://doi.org/10.1609/aaai.v36i2.20113
Publikováno v:
Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a56aac8a9a12e696055387b53c2ced23
https://doi.org/10.1145/3571600.3571652
https://doi.org/10.1145/3571600.3571652
Autor:
Mohesh Babu Radhakrishnan
Mental health concerns are evident among tertiary students in New Zealand. However, some tertiary learning institutions seldom notice the crucial role to design environmental abilities to enhance such well-being. Research says that the human body def
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a22e274cba302893b2b292b2dc37d98
https://doi.org/10.26686/wgtn.20060321
https://doi.org/10.26686/wgtn.20060321
Publikováno v:
IEEE Transactions on Computational Imaging. 7:1228-1239
We propose a novel recurrent network-based HDR deghosting method for fusing arbitrary length dynamic sequences. The proposed method uses convolutional and recurrent architectures to generate visually pleasing, ghosting-free HDR images. We introduce a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cac7a85ddf3a66ce4f155d08672a5a18
https://doi.org/10.1109/tci.2021.3112920
https://doi.org/10.1109/tci.2021.3112920
Publikováno v:
J Neurosci
We present a biologically inspired recurrent neural network (RNN) that efficiently detects changes in natural images. The model features sparse, topographic connectivity (st-RNN), closely modeled on the circuit architecture of a “midbrain attention
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0a685871e3466f42eed899a45551aba
https://pubmed.ncbi.nlm.nih.gov/36123120
https://pubmed.ncbi.nlm.nih.gov/36123120
Autor:
Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Varun Jampani, Venkatesh Babu Radhakrishnan
Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eebd31965f4f8021b968a1378871aa45
Autor:
Venkatesh Babu Radhakrishnan
Publikováno v:
2020 International Conference on Signal Processing and Communications (SPCOM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0c78d098d846bb9e2f8979380e78df6
https://doi.org/10.1109/spcom50965.2020.9179615
https://doi.org/10.1109/spcom50965.2020.9179615
Autor:
Mugalodi Rakesh, Anirban Chakraborty, M V Rahul, Jogendra Nath Kundu, Venkatesh Babu Radhakrishnan, Siddharth Seth
Publikováno v:
AAAI
Scopus-Elsevier
Scopus-Elsevier
Estimation of 3D human pose from monocular image has gained considerable attention, as a key step to several human-centric applications. However, generalizability of human pose estimation models developed using supervision on large-scale in-studio da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f36a63b41d37052a07cfea37fe42be7
http://arxiv.org/abs/2006.14107
http://arxiv.org/abs/2006.14107
Publikováno v:
ICCV
Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image samples with imperceptible noise engineered to manipulate the network's pred
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::183a4b7b3e43006b4e5d25eb571c01d0
https://doi.org/10.1109/iccv.2019.00816
https://doi.org/10.1109/iccv.2019.00816