Zobrazeno 1 - 10
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pro vyhledávání: '"Abavisani, Mahdi"'
Publikováno v:
Conference on Computer Vision and Pattern Recognition (CVPR 2020)
Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time. Emergency response is one
Externí odkaz:
http://arxiv.org/abs/2004.04917
Autor:
Abavisani, Mahdi, Patel, Vishal M.
Publikováno v:
IEEE Signal Processing Letters, 2019
We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. The role of the autoencoder ne
Externí odkaz:
http://arxiv.org/abs/1904.11093
Publikováno v:
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1165-1174
We present an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand gesture recognition. Instead of explicitly combining multimodal inform
Externí odkaz:
http://arxiv.org/abs/1812.06145
Autor:
Abavisani, Mahdi, Patel, Vishal M.
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, pp. 1601-1614, Dec. 2018
We present convolutional neural network (CNN) based approaches for unsupervised multimodal subspace clustering. The proposed framework consists of three main stages - multimodal encoder, self-expressive layer, and multimodal decoder. The encoder take
Externí odkaz:
http://arxiv.org/abs/1804.06498
In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. In this paper, we propose an extension of the unsupervised image-to-image translation pro
Externí odkaz:
http://arxiv.org/abs/1711.09334
Autor:
Abavisani, Mahdi, Patel, Vishal M.
Publikováno v:
In Information Fusion January 2018 39:168-177
Autor:
Abavisani, Mahdi
Recent advances in technology have provided massive amounts of complex high-dimensional and multimodal data for computer vision and machine learning applications. This thesis uses sparse and low-rank representation-based techniques to introduce sever
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ae14a510f09262266dabb66fb2125bd
Autor:
Joneidi, Mohsen, Zaeemzadeh, Alireza, Rezaeifar, Shideh, Abavisani, Mahdi, Rahnavard, Nazanin
Publikováno v:
2015 49th Annual Conference on Information Sciences & Systems (CISS); 2015, p1-5, 5p
Publikováno v:
2015 IEEE Signal Processing in Medicine & Biology Symposium (SPMB); 1/1/2015, p1-6, 6p
Akademický článek
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