Zobrazeno 1 - 10
of 637
pro vyhledávání: '"CROWLEY, JAMES P."'
Publikováno v:
Affective Computing and Intelligent Interaction (ACII), Sep 2023, Cambridge (MA), United States
Decades of research indicate that emotion recognition is more effective when drawing information from multiple modalities. But what if some modalities are sometimes missing? To address this problem, we propose a novel Transformer-based architecture f
Externí odkaz:
http://arxiv.org/abs/2311.10119
Publikováno v:
ACII 2022 - 10th International Conference on Affective Computing and Intelligent Interaction, Oct 2022, Nara, Japan
In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this task. In addition, we present how such models may be pretrained in a mu
Externí odkaz:
http://arxiv.org/abs/2212.13885
Autor:
Crowley, James L., Coutaz, Joëlle L, Grosinger, Jasmin, Vázquez-Salceda, Javier, Angulo, Cecilio, Sanfeliu, Alberto, Iocchi, Luca, Cohn, Anthony G.
Publikováno v:
IEEE Pervasive Computing, 2022
We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilitie
Externí odkaz:
http://arxiv.org/abs/2212.08659
Flare occurrence on the Sun is highly variable, exhibiting both short term variation due to the emergence and evolution of active regions, and long-term variation from the solar cycle. On solar-like stars, much larger stellar flares (superflares) hav
Externí odkaz:
http://arxiv.org/abs/2212.00993
Autor:
Wang, Yangtao, Shen, Xi, Yuan, Yuan, Du, Yuming, Li, Maomao, Hu, Shell Xu, Crowley, James L, Vaufreydaz, Dominique
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that compose an image or video are o
Externí odkaz:
http://arxiv.org/abs/2209.00383
Publikováno v:
26th International Conference on Pattern Recognition (ICPR 2022), Aug 2022, Montreal, Canada
In order to exploit representations of time-series signals, such as physiological signals, it is essential that these representations capture relevant information from the whole signal. In this work, we propose to use a Transformer-based model to pro
Externí odkaz:
http://arxiv.org/abs/2204.05103
Autor:
Schüller, Peter, Costeira, João Paolo, Crowley, James, Grosinger, Jasmin, Ingrand, Félix, Köckemann, Uwe, Saffiotti, Alessandro, Welss, Martin
Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS for robotics. We describe an ex
Externí odkaz:
http://arxiv.org/abs/2202.12566
Publikováno v:
CVPR 2022 - Conference on Computer Vision and Pattern Recognition, Jun 2022, New Orleans, United States
Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this paper, we demonstrate a graph-based approach that uses the self-superv
Externí odkaz:
http://arxiv.org/abs/2202.11539
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Akademický článek
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