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6 results on '"Valstar, Michel"'

1. A demonstration of audiovisual sensitive artificial listeners

Author

Schröder, Marc, Bevacqua, Elisabetta, Eyben, Florian, Gunes, Hatice, Heylen, Dirk, ter Maat, Mark, Pammi, Sathish, Pantic, Maja, Pelachaud, Catherine, Schuller, Björn, de Sevin, Etienne, Valstar, Michel, Wöllmer, Martin, Mühl, Christian, and Nijholt, Anton

Subjects
Markup language, HMI-CI: Computational Intelligence, Computer science, InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), media_common.quotation_subject, EWI-17322, METIS-264503, Face (sociological concept), Virtual agent, HMI-MI: MULTIMODAL INTERACTIONS, 02 engineering and technology, computer.software_genre, Virtual Agent, Listening agent, HMI-IA: Intelligent Agents, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Personality, Conversation, 050207 economics, Audio signal processing, Affective computing, ECA, media_common, Communication, business.industry, 05 social sciences, 020206 networking & telecommunications, EC Grant Agreement nr.: FP7/211486, Expression (mathematics), Affective Computing, IR-69688, ddc:004, business, computer
Abstract

Sensitive Artificial Listeners (SAL) are virtual dialogue partners who, despite their very limited verbal understanding, intend to engage the user in a conversation by paying attention to the user's emotions and non-verbal expressions. The SAL characters have their own emotionally defined personality, and attempt to drag the user towards their dominant emotion, through a combination of verbal and non-verbal expression. The demonstrator shows an early version of the fully autonomous SAL system based on audiovisual analysis and synthesis.

Published
2020

2. The Visual Object Tracking VOT2015 Challenge Results

Author

Kristan, Matej, Matas, Jiri, Leonardis, Aleš, Felsberg, Michael, Cehovin, Luka, Fernández, Gustavo, Vojir, Tomas, Häger, Gustav, Nebehay, Georg, Pflugfelder, Roman, Gupta, Abhinav, Bibi, Adel, Lukežič, Alan, Garcia-Martin, Alvaro, Saffari, Amir, Petrosino, Alfredo, Montero, Andrés, Varfolomieiev, Anton, Baskurt, Atilla, Zhao, Baojun, Ghanem, Bernard, Martinez, Brais, Lee, Byeongju, Han, Bohyung, Wang, Chaohui, Garcia, Christophe, Zhang, Chunyuan, Schmid, Cordelia, Tao, Dacheng, Kim, Daijin, Huang, Dafei, Prokhorov, Danil, Du, Dawei, Yeung, Dit-Yan, Ribeiro, Eraldo, Shahbaz Khan, Fahad, Porikli, Fatih, Bunyak, Filiz, Zhu, Gao, Seetharaman, Guna, Kieritz, Hilke, Yau, Hing, Li, Hongdong, Qi, Honggang, Bischof, Horst, Possegger, Horst, Lee, Hyemin, Nam, Hyeonseob, Bogun, Ivan, Jeong, Jae-Chan, Cho, Jae-Il, Lee, Jae-Yeong, Zhu, Jianke, Shi, Jianping, Li, Jiatong, Jia, Jiaya, Feng, Jiayi, Gao, Jin, Choi, Jin Young, Kim, Ji-Wan, Lang, Jochen, Martinez, Jose M., Choi, Jongwon, Xing, Junliang, Xue, Kai, Palaniappan, Kannappan, Lebeda, Karel, Alahari, Karteek, Gao, Ke, Yun, Kimin, Wong, Kin Hong, Luo, Lei, Ma, Liang, Ke, Lipeng, Wen, Longyin, Bertinetto, Luca, Pootschi, Mahdieh, Maresca, Mario, Danelljan, Martin, Wen, Mei, Zhang, Mengdan, Arens, Michael, Valstar, Michel, Tang, Ming, Chang, Ming-Ching, Khan, Muhammad, Fan, Nana, Wang, Naiyan, Miksik, Ondrej, Torr, Philip H. S., Wang, Qiang, Martin-Nieto, Rafael, Pelapur, Rengarajan, Bowden, Richard, Laganière, Robert, Moujtahid, Salma, Hare, Sam, Hadfield, Simon, Lyu, Siwei, Li, Siyi, Zhu, Song-Chun, Becker, Stefan, Duffner, Stefan, Hicks, Stephen, Golodetz, Stuart, Choi, Sunglok, Wu, Tianfu, Mauthner, Thomas, Pridmore, Tony, Hu, Weiming, Hübner, Wolfgang, Wang, Xiaomeng, Li, Xin, Shi, Xinchu, Zhao, Xu, Mei, Xue, Shizeng, Yao, Hua, Yang, Li, Yang, Lu, Yang, Li, Yuezun, Chen, Zhaoyun, Huang, Zehua, Chen, Zhe, Zhang, Zhe, He, Zhenyu, Hong, Zhibin, Duffner, Stefan, University of Ljubljana, Czech Technical University in Prague (CTU), University of Birmingham [Birmingham], Linköping University (LIU), Austrian Institute of Technology [Vienna] (AIT), Carnegie Mellon University [Pittsburgh] (CMU), King Abdullah University of Science and Technology (KAUST), Universidad Autónoma de Madrid (UAM), Affectv [London], Università degli Studi di Napoli 'Parthenope' = University of Naples (PARTHENOPE), University of Ottawa [Ottawa], National Technical University of Ukraine 'Kyiv Polytechnic Institute' [Kiev], Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Beijing Institute of Technology (BIT), Faculty of Engineering [Nottingham], University of Nottingham, UK (UON), Seoul National University [Seoul] (SNU), Pohang University of Science and Technology (POSTECH), Université Paris-Est (UPE), National University of Defense Technology [China], Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), University of Technology Sydney (UTS), National Key Laboratory for Parallel and Distributed Processing [Changsha], Toyota Research Institute, Chinese Academy of Sciences [Changchun Branch] (CAS), University at Albany [SUNY], State University of New York (SUNY), Hong Kong University of Science and Technology (HKUST), Florida Institute of Technology [Melbourne], Australian National University (ANU), Networks Research Group (NICTA), National ICT Australia Ltd, University of Missouri [Columbia] (Mizzou), University of Missouri System, Naval Research Laboratory (NRL), Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB), Fraunhofer (Fraunhofer-Gesellschaft), The Chinese University of Hong Kong [Hong Kong], Centre of Excellence for Robotic Vision [Canberra], Australian Research Council [Canberra] (ARC), Graz University of Technology [Graz] (TU Graz), Electronics and Telecommunications Research Institute [DaeJeon] (ETRI), Zhejiang University, Institute of Automation - Chinese Academy of Sciences, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering [Korea Advanced Institute of Science and Technology] (KAIST), Korea Advanced Institute of Science and Technology (KAIST), Centre Maurice Halbwachs (CMH), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département de Sciences sociales ENS-PSL, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Harbin Engineering University (HRBEU), University of Surrey (UNIS), University of Oxford, Harbin Institute of Technology (HIT), Obvious Enginerring, Laboratoire de Physique Corpusculaire - Clermont-Ferrand (LPC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), University of California [Los Angeles] (UCLA), University of California (UC), Laboratoire Interfaces et Systèmes Electrochimiques (LISE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Electronics and Telecommunications Research Institute, Rice University [Houston], University of New Orleans, National Key Laboratory of Crop Genetic Improvement, Sichuan University, College of Life Sciences-Huazhong Agricultural University [Wuhan] (HZAU), Key Laboratory for Anisotropy and Texture of Materials, Northeastern University [Shenyang], Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de recherche pour le développement [IRD] : UR206-Centre National de la Recherche Scientifique (CNRS), School of Materials Science and Engineering, Shanghai University, State Key Laboratory of Marine Geology [Shanghai], Tongji University, UAM. Departamento de Tecnología Electrónica y de las Comunicaciones, Tratamiento e Interpretación de Vídeo (ING EPS-006), Universidad Autonoma de Madrid (UAM), Universita degli studi di Napoli 'Parthenope' [Napoli], Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), University of Oxford [Oxford], University of California, The University of New Orleans, Sichuan University, College of Life Sciences-Huazhong Agricultural University, and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de recherche pour le développement [IRD] : UR206-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Target tracking, Signalbehandling, 02 engineering and technology, Object tracking, Tracking (particle physics), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Image processing, Datorseende och robotik (autonoma system), 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Computer Vision and Robotics (Autonomous Systems), Visualization, Benchmark testing, Telecomunicaciones, business.industry, Tracking, Australia, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Object (computer science), Area measurement, Benchmarking, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Performance evaluation, Video tracking, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Signal Processing, 020201 artificial intelligence & image processing, Artificial intelligence, Radiologi och bildbehandling, business, Flow visualization, Radiology, Nuclear Medicine and Medical Imaging
Abstract

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Kristan et al., "The Visual Object Tracking VOT2015 Challenge Results," 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, 2015, pp. 564-586. doi: 10.1109/ICCVW.2015.79, The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

Published
2015

3. Cost-effective solution to synchronised audio-visual data capture using multiple sensors

Author

Lee, T., Lichtenauer, Jeroen, Soatto, S., Shen, Jie, Valstar, Michel, and Pantic, Maja

Subjects
Audio recording, Offset (computer science), EC Grant Agreement nr.: ERC/203143, Computer science, METIS-284996, Real-time computing, Automatic identification and data capture, Serial port, HMI-MI: MULTIMODAL INTERACTIONS, 02 engineering and technology, EWI-21248, Synchronisation, 0202 electrical engineering, electronic engineering, information engineering, IR-79390, business.industry, 020207 software engineering, Sensor fusion, EC Grant Agreement nr.: FP7/211486, Sound recording and reproduction, Video recording, Asynchronous communication, Temporal resolution, Signal Processing, Multisensor systems, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Timestamp, business, Computer hardware
Abstract

Applications such as surveillance and human behaviour analysis require high-bandwidth recording from multiple cameras, as well as from other sensors. In turn, sensor fusion has increased the required accuracy of synchronisation between sensors. Using commercial off-the-shelf components may compromise quality and accuracy due to several challenges, such as dealing with the combined data rate from multiple sensors; unknown offset and rate discrepancies between independent hardware clocks; the absence of trigger inputs or -outputs in the hardware; as well as the different methods for time-stamping the recorded data. To achieve accurate synchronisation, we centralise the synchronisation task by recording all trigger- or timestamp signals with a multi-channel audio interface. For sensors that don't have an external trigger signal, we let the computer that captures the sensor data periodically generate timestamp signals from its serial port output. These signals can also be used as a common time base to synchronise multiple asynchronous audio interfaces. Furthermore, we show that a consumer PC can currently capture 8-bit video data with 1024 × 1024 spatial- and 59.1 Hz temporal resolution, from at least 14 cameras, together with 8 channels of 24-bit audio at 96 kHz. We thus improve the quality/cost ratio of multi-sensor systems data capture systems.

Published
2011
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4. Transductive Multi-label Zero-shot Learning

Author

Timothy M. Hospedales, Tao Xiang, Yanwei Fu, Shaogang Gong, Yongxin Yang, Valstar, Michel, French, Andrew, and Pridmore, Tony

Subjects
FOS: Computer and information sciences, Exploit, Property (programming), Computer science, business.industry, Principle of compositionality, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Contrast (statistics), Space (commercial competition), Variety (linguistics), Machine learning, computer.software_genre, Machine Learning (cs.LG), Image (mathematics), Computer Science - Learning, Artificial intelligence, business, computer, Word (computer architecture)
Abstract

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines., 12 pages, 6 figures, Accepted to BMVC 2014 (oral)

Published
2015

5. Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation

Author

Lubomir Bourdev, Bo Chen, Pietro Perona, Valstar, Michel, French, Andrew, and Pridmore, Tony

Subjects
Hierarchy, Speedup, business.industry, Computer science, Pattern recognition, Pascal (programming language), Tree structure, Cascade, Action recognition, Segmentation, Artificial intelligence, business, Pose, computer, computer.programming_language
Abstract

Poselets have been used in a variety of computer vision tasks, such as detection, segmentation, action classification, pose estimation and action recognition, often achieving state-of-the-art performance. Poselet evaluation, however, is computationally intensive as it involves running thousands of scanning window classifiers. We present an algorithm for training a hierarchical cascade of part-based detectors and apply it to speed up poselet evaluation. Our cascade hierarchy leverages common components shared across poselets. We generate a family of cascade hierarchies, including trees that grow logarithmically on the number of poselet classifiers. Our algorithm, under some reasonable assumptions, finds the optimal tree structure that maximizes speed for a given target detection rate. We test our system on the PASCAL dataset and show an order of magnitude speedup at less than 1% loss in AP.

Published
2014

6. Is 2D information enough for viewpoint estimation?

Author

Amir Ghodrati, Marco Pedersoli, Tinne Tuytelaars, Valstar, Michel François, French, Andrew P, and Pridmore, Tony P

Subjects
Class (computer programming), Computer science, business.industry, CAD, PSI_VISICS, Object (computer science), Viewpoints, Machine learning, computer.software_genre, Convolutional neural network, Minimum bounding box, Encoding (memory), Artificial intelligence, business, Representation (mathematics), computer
Abstract

© 2014. The copyright of this document resides with its authors. Recent top performing methods for viewpoint estimation make use of 3D information like 3D CAD models or 3D landmarks to build a 3D representation of the class. These 3D annotations are expensive and not really available for many classes. In this paper we investigate whether and how comparable performance can be obtained without any 3D information. We consider viewpoint estimation as a 1-vs-all classification problem on the previously detected object bounding box. In this framework we compare several features and parameter configurations and show that the modern representations based on Fisher encoding and convolutional neural network based features together with a neighbor viewpoints suppression strategy on the training data lead to comparable or even better performance than 3D methods. Ghodrati A., Pedersoli M., Tuytelaars T., ''Is 2D information enough for viewpoint estimation?'', 25th British machine vision conference - BMVC 2014, 12 pp., September 1-5, 2014, Nottingham, UK. ispartof: pages:1-12 ispartof: Proceedings BMVC 2014 pages:1-12 ispartof: British machine vision conference - BMVC 2014 location:Nottingham, UK. date:1 Sep - 5 Sep 2014 status: published

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