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Raquel Justo; Leila Ben Letaifa; Cristina Palmero; Eduardo Gonzalez-Fraile; Anna Torp Johansen; Alain Vazquez; Gennaro Cordasco; Stephan Schlogl; Begoña Fernandez-Ruanova; Micaela Silva; Sergio Escalera; Mikel de Velasco; Joffre Tenorio-Laranga; Anna Esposito; Maria Korsnes; M. Ines Torres |
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Title |
Analysis of the Interaction between Elderly People and a Simulated Virtual Coach, Journal of Ambient Intelligence and Humanized Computing |
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Journal Article |
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2020 |
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Journal of Ambient Intelligence and Humanized Computing |
Abbreviated Journal |
AIHC |
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11 |
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12 |
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6125-6140 |
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The EMPATHIC project develops and validates new interaction paradigms for personalized virtual coaches (VC) to promote healthy and independent aging. To this end, the work presented in this paper is aimed to analyze the interaction between the EMPATHIC-VC and the users. One of the goals of the project is to ensure an end-user driven design, involving senior users from the beginning and during each phase of the project. Thus, the paper focuses on some sessions where the seniors carried out interactions with a Wizard of Oz driven, simulated system. A coaching strategy based on the GROW model was used throughout these sessions so as to guide interactions and engage the elderly with the goals of the project. In this interaction framework, both the human and the system behavior were analyzed. The way the wizard implements the GROW coaching strategy is a key aspect of the system behavior during the interaction. The language used by the virtual agent as well as his or her physical aspect are also important cues that were analyzed. Regarding the user behavior, the vocal communication provides information about the speaker’s emotional status, that is closely related to human behavior and which can be extracted from the speech and language analysis. In the same way, the analysis of the facial expression, gazes and gestures can provide information on the non verbal human communication even when the user is not talking. In addition, in order to engage senior users, their preferences and likes had to be considered. To this end, the effect of the VC on the users was gathered by means of direct questionnaires. These analyses have shown a positive and calm behavior of users when interacting with the simulated virtual coach as well as some difficulties of the system to develop the proposed coaching strategy. |
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HuPBA; no proj |
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Admin @ si @ JLP2020 |
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3443 |
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Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
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Gate-Shift-Fuse for Video Action Recognition |
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2023 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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45 |
Issue |
9 |
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10913-10928 |
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Action Recognition; Video Classification; Spatial Gating; Channel Fusion |
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Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks. |
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1 Sept. 2023 |
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HUPBA; no menciona |
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Admin @ si @ SEL2023 |
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3814 |
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Victor M. Campello; Carlos Martin-Isla; Cristian Izquierdo; Andrea Guala; Jose F. Rodriguez Palomares; David Vilades; Martin L. Descalzo; Mahir Karakas; Ersin Cavus; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Karim Lekadir |
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Minimising multi-centre radiomics variability through image normalisation: a pilot study |
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2022 |
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Scientific Reports |
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ScR |
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12 |
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1 |
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12532 |
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Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification. |
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2022/07/22 |
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Springer Nature |
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HuPBA |
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Admin @ si @ CMI2022 |
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3749 |
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Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes |
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Video transformers: A survey |
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Journal Article |
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2023 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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45 |
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11 |
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12922-12943 |
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Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations |
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Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. |
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1 Nov. 2023 |
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HUPBA; no menciona |
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no |
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Admin @ si @ SJE2023 |
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3823 |
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Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
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Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams |
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2016 |
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Multimedia Tools and Applications |
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MTAP |
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75 |
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22 |
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14985-14990 |
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ISE; HUPBA |
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Admin @ si @ DDB2016 |
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2934 |
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