by Xavier Alameda-Pineda, Vincent Drouard, Radu Horaud IEEE TNNLS 2021 [PDF] [arXiv] Abstract Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by…
by Guillaume Delorme , Yutong Ban , Guillaume Sarrazin and Xavier Alameda-Pineda ICPR’20 Workshop on Multimodal pattern recognition for social signal processing in human computer interaction [paper] Abstract. The analysis of effective states through time in multi-person scenarii is very challenging, because it requires to consistently track all persons over time. This requires…
by Dan Xu, Xavier Alameda-Pineda, Wanli Ouyang, Elisa Ricci, Xiaogang Wang and Nicu Sebe IEEE TPAMI, 2020 [paper] [arXiv] Abstract. Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present a novel approach that advances the state of…
