6 citations · 23 across the 9 of their papers we have counts for
9 papers
Learning Multiple Sound Source 2D Localization
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue +4
In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound s…
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization
Guillaume Le Moing, Don Joven Agravante, Tadanobu Inoue +4
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neura…
Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization
Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante +4
Deep neural networks have recently led to promising results for the task of multiple sound source localization. Yet, they require a lot of training data to cover a variety of acous…
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos
Subhajit Chaudhury, Daiki Kimura, Phongtharin Vinayavekhin +7
Image-based sports analytics enable automatic retrieval of key events in a game to speed up the analytics process for human experts. However, most existing methods focus on structu…
Unifying Heterogeneous Classifiers with Distillation
Jayakorn Vongkulbhisal, Phongtharin Vinayavekhin, Marco Visentini-Scarzanella
In this paper, we study the problem of unifying knowledge from a set of classifiers with different architectures and target classes into a single classifier, given only a generic s…
Deep Learning with Predictive Control for Human Motion Tracking
Don Joven Agravante, Giovanni De Magistris, Asim Munawar +2
We propose to combine model predictive control with deep learning for the task of accurate human motion tracking with a robot. We design the MPC to allow switching between the lear…
Experimental Force-Torque Dataset for Robot Learning of Multi-Shape Insertion
Giovanni De Magistris, Asim Munawar, Tu-Hoa Pham +3
The accurate modeling of real-world systems and physical interactions is a common challenge towards the resolution of robotics tasks. Machine learning approaches have demonstrated…
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
Phongtharin Vinayavekhin, Subhajit Chaudhury, Asim Munawar +4
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series. We propose a temporal attention layer that is capable of…
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
Asim Munawar, Phongtharin Vinayavekhin, Giovanni De Magistris
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on…