Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Peng, W. (Wei)"'
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Spatial-temporal graph convolutional networks (ST-GCNs) have been successfully applied for dynamic graphs representation learning, such as modeling skeleton-based human actions. However, ST-GCNs embed these non-Euclidean graph structures into Euclide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a553b166de010fa03db03d168c2b045c
https://doi.org/10.1109/icip46576.2022.9897522
https://doi.org/10.1109/icip46576.2022.9897522
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred the development of various acceleration methods, typically through heuristically undersampling the MRI measurement domain known as k-space. Recently, deep neural networks h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c89a8eaef67008fd28d72e5e5ec68191
https://doi.org/10.1109/cvpr52688.2022.02013
https://doi.org/10.1109/cvpr52688.2022.02013
Autor:
Peng, W. (Wei)
Understanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::7991ec81e7d88d984c0d834fb794a92b
http://urn.fi/urn:isbn:9789526232591
http://urn.fi/urn:isbn:9789526232591
Graph Convolutional Network (GCN) has already been successfully applied to skeleton-based action recognition. However, current GCNs in this task are lack of pooling operations such that the architectures are inherently flat, which not only increases
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::10dd8260d0ebe4691e6e21c585f6fd37
http://urn.fi/urn:nbn:fi-fe2021041410351
http://urn.fi/urn:nbn:fi-fe2021041410351
Multimodal sentiment analysis has increasingly attracted attention since with the arrival of complementary data streams, it has great potential to improve and go beyond unimodal sentiment analysis. In this paper, we present an efficient separable mul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::888948562ff5e1becbdc12676cd0e075
http://urn.fi/urn:nbn:fi-fe202104099805
http://urn.fi/urn:nbn:fi-fe202104099805