Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Parisi, German I."'
Understanding human affective behaviour, especially in the dynamics of real-world settings, requires Facial Expression Recognition (FER) models to continuously adapt to individual differences in user expression, contextual attributions, and the envir
Externí odkaz:
http://arxiv.org/abs/2305.06448
Autor:
Lomonaco, Vincenzo, Pellegrini, Lorenzo, Cossu, Andrea, Carta, Antonio, Graffieti, Gabriele, Hayes, Tyler L., De Lange, Matthias, Masana, Marc, Pomponi, Jary, van de Ven, Gido, Mundt, Martin, She, Qi, Cooper, Keiland, Forest, Jeremy, Belouadah, Eden, Calderara, Simone, Parisi, German I., Cuzzolin, Fabio, Tolias, Andreas, Scardapane, Simone, Antiga, Luca, Amhad, Subutai, Popescu, Adrian, Kanan, Christopher, van de Weijer, Joost, Tuytelaars, Tinne, Bacciu, Davide, Maltoni, Davide
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning co
Externí odkaz:
http://arxiv.org/abs/2104.00405
Autor:
Lomonaco, Vincenzo, Pellegrini, Lorenzo, Rodriguez, Pau, Caccia, Massimo, She, Qi, Chen, Yu, Jodelet, Quentin, Wang, Ruiping, Mai, Zheda, Vazquez, David, Parisi, German I., Churamani, Nikhil, Pickett, Marc, Laradji, Issam, Maltoni, Davide
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the signific
Externí odkaz:
http://arxiv.org/abs/2009.09929
Autor:
She, Qi, Feng, Fan, Liu, Qi, Chan, Rosa H. M., Hao, Xinyue, Lan, Chuanlin, Yang, Qihan, Lomonaco, Vincenzo, Parisi, German I., Bae, Heechul, Brophy, Eoin, Chen, Baoquan, Graffieti, Gabriele, Goel, Vidit, Han, Hyonyoung, Kanagarajah, Sathursan, Kumar, Somesh, Lam, Siew-Kei, Lam, Tin Lun, Ma, Liang, Maltoni, Davide, Pellegrini, Lorenzo, Piyasena, Duvindu, Pu, Shiliang, Sheet, Debdoot, Song, Soonyong, Son, Youngsung, Wang, Zhengwei, Ward, Tomas E., Wu, Jianwen, Wu, Meiqing, Xie, Di, Xu, Yangsheng, Yang, Lin, Zhong, Qiaoyong, Zhou, Liguang
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenL
Externí odkaz:
http://arxiv.org/abs/2004.14774
Autor:
Parisi, German I., Lomonaco, Vincenzo
Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples. Learning continually in a single data pass is crucial for agents
Externí odkaz:
http://arxiv.org/abs/2003.09114
Autor:
Parisi, German I.
The robust recognition and assessment of human actions are crucial in human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable results in large-scale action datasets, they mostly lack the flexibility,
Externí odkaz:
http://arxiv.org/abs/2001.05837
Autor:
Parisi, German I., Kanan, Christopher
Continual learning refers to the ability of a biological or artificial system to seamlessly learn from continuous streams of information while preventing catastrophic forgetting, i.e., a condition in which new incoming information strongly interferes
Externí odkaz:
http://arxiv.org/abs/1907.01929
Publikováno v:
in PMLR 97:485-494 (2019)
Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when recognizing online and personalized facial expressions, e.g., for person-
Externí odkaz:
http://arxiv.org/abs/1904.12632
Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work, we demonstrate that dynamically grown networks outperform static netw
Externí odkaz:
http://arxiv.org/abs/1811.02113
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment. However, artificial multisensory perception remains an open issue especially in situations of sensory uncer
Externí odkaz:
http://arxiv.org/abs/1810.06748