Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding,  Hao Tang, Xavier Alameda-Pineda, Elisa Ricci IEEE Transactions on Multimedia [arXiv][HAL] Abstract. Over the past years, semantic segmentation, similar to many other tasks in computer vision, has benefited from the progress in deep neural networks, resulting in significantly improved performance. However, deep architectures trained…

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A Proposal-based Paradigm for Self-supervised Sound Source Localization in Videos

Hanyu Xuan, Zhiliang Wu, Jian Yang, Yan Yan, Xavier Alameda-Pineda IEEE/CVF International Conference on Computer Vision (CVPR) 2022, New Orleans, US [HAL] Abstract. Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues. To achieve such cross-modal perception on machines, existing methods…

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Continual Models are Self-Supervised Learners

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,…

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The impact of removing head movements on audio-visual speech enhancement

by Zhiqi Kang, Mostafa Sadeghi, Radu Horaud, Xavier Alameda-Pineda, Jacob Donley, Anurag Kumar ICASSP’22, Singapore [paper][examples][code][slides] Abstract. This paper investigates the impact of head movements on audio-visual speech enhancement (AVSE). Although being a common conversational feature, head movements have been ignored by past and recent studies: they challenge today’s learning-based…

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Dynamical Variational AutoEncoders

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…

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SocialInteractionGAN: Multi-person Interaction Sequence Generation

by Louis Airale, Dominique Vaufreydaz and Xavier Alameda-Pineda [paper] Abstract. Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction…

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PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimation

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,…

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Robust Face Frontalization For Visual Speech Recognition

by Zhiqi Kang, Radu Horaud and Mostafa Sadeghi ICCV’21 Workshop on Traditional Computer Vision in the Age of Deep Learning (TradiCV’21) [paper (extended version)][code][bibtex] Abstract. Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution is a robust method that preserves non-rigid facial deformations, i.e….

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