by Wen Guo*, Yuming Du*, Xi Shen, Vincent Lepetit, Xavier Alameda-Pineda, and Francesc Moreno-Noguer IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023, Waikoloa, Hawaii [paper] [code] [HAL] Abstract. This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. State-of-the-art…
by Samir Sadok, Simon Leglaive, Laurent Girin, Xavier Alameda-Pineda, Renaud Séguier SpeechCom, 2023 [arXiv] [HAL] [code] [examples] Abstract: Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms…
by Anand Ballou, Xavier Alameda-Pineda, and Chris Reinke Applied Intelligence [paper][code] Abstract: With the increasing presence of robots in our everyday environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors often need to be adapted, as social…
by Chris Reinke and Xavier Alameda-Pineda Transactions on Machine Learning Research [Paper][Code] Abstract. Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor features (SF) are a prominent transfer mechanism in domains where the reward function changes between tasks. They reevaluate…
By Guillaume Delorme*, Yihong Xu*, Luis G. Camara, Elisa Ricci, Radu Horaud, Xavier Alameda Pineda [arXiv] [paper] [code] Abstract: Existing works on multiple object tracking (MOT) are developed under the traditional supervised learning setting, where the training and test data are drawn from the same distribution. This hinders the development…
by Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, New Orleans, USA [arXiv][Code][HAL] Abstract. Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However,…
by Davier Emukpere, Xavier Alameda-Pineda and Chris Reinke [Paper][code] Abstract. A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: episodic control and successor features. Episodic…
by Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, and Xavier Alameda-Pineda Foundations and Trends in Machine Learning, 2021, Vol. 15, No. 1-2, pp 1–175. [Review paper] [Code] [Tutorial @ICASPP 2021] Abstract. Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional…
by Xiaoyu Bie, Laurent Girin, Simon Leglaive, Thomas Hueber and Xavier Alameda-Pineda Interspeech’21, Brno, Czech Republic [paper][slides][code][bibtex] Abstract. The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the…
by Wen Guo, Enric Corona, Francesc Moreno-Noguer, Xavier Alameda-Pineda, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2021) [paper][code] Abstract. Recent literature addressed the monocular 3D pose estimation task very satisfactorily. In these studies, different persons are usually treated as independent pose instances to estimate. However, in many everyday situations,…
