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1,040 results on '"Pantic, Maja"'

151. Dynamic Probabilistic CCA for Analysis of Affective Behaviour

Author

Nicolaou, Mihalis A., Pavlovic, Vladimir, Pantic, Maja, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fitzgibbon, Andrew, editor, Lazebnik, Svetlana, editor, Perona, Pietro, editor, Sato, Yoichi, editor, and Schmid, Cordelia, editor

Published
2012
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152. Continuous Pain Intensity Estimation from Facial Expressions

Author

Kaltwang, Sebastian, Rudovic, Ognjen, Pantic, Maja, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Fowlkes, Charless, editor, Wang, Sen, editor, Choi, Min-Hyung, editor, Mantler, Stephan, editor, Schulze, Jürgen, editor, Acevedo, Daniel, editor, Mueller, Klaus, editor, and Papka, Michael, editor

Published
2012
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155. A Multimodal Database for Mimicry Analysis

Author

Sun, Xiaofan, Lichtenauer, Jeroen, Valstar, Michel, Nijholt, Anton, Pantic, Maja, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, D’Mello, Sidney, editor, Graesser, Arthur, editor, Schuller, Björn, editor, and Martin, Jean-Claude, editor

Published
2011
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156. AVEC 2011–The First International Audio/Visual Emotion Challenge

Author

Schuller, Björn, Valstar, Michel, Eyben, Florian, McKeown, Gary, Cowie, Roddy, Pantic, Maja, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, D’Mello, Sidney, editor, Graesser, Arthur, editor, Schuller, Björn, editor, and Martin, Jean-Claude, editor

Published
2011
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157. Automatic Understanding of Affective and Social Signals by Multimodal Mimicry Recognition

Author

Sun, Xiaofan, Nijholt, Anton, Truong, Khiet P., Pantic, Maja, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, D’Mello, Sidney, editor, Graesser, Arthur, editor, Schuller, Björn, editor, and Martin, Jean-Claude, editor

Published
2011
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158. Social Signal Processing: The Research Agenda

Author

Pantic, Maja, Cowie, Roderick, D’Errico, Francesca, Heylen, Dirk, Mehu, Marc, Pelachaud, Catherine, Poggi, Isabella, Schroeder, Marc, Vinciarelli, Alessandro, Moeslund, Thomas B., editor, Hilton, Adrian, editor, Krüger, Volker, editor, and Sigal, Leonid, editor

Published
2011
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162. Dimensional Emotion Prediction from Spontaneous Head Gestures for Interaction with Sensitive Artificial Listeners

Author

Gunes, Hatice, Pantic, Maja, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Allbeck, Jan, editor, Badler, Norman, editor, Bickmore, Timothy, editor, Pelachaud, Catherine, editor, and Safonova, Alla, editor

Published
2010
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163. Coupled Gaussian Process Regression for Pose-Invariant Facial Expression Recognition

Author

Rudovic, Ognjen, Patras, Ioannis, Pantic, Maja, Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Daniilidis, Kostas, editor, Maragos, Petros, editor, and Paragios, Nikos, editor

Published
2010
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166. Decision-Level Fusion for Audio-Visual Laughter Detection

Author

Reuderink, Boris, Poel, Mannes, Truong, Khiet, Poppe, Ronald, Pantic, Maja, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Popescu-Belis, Andrei, editor, and Stiefelhagen, Rainer, editor

Published
2008
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169. Combined Support Vector Machines and Hidden Markov Models for Modeling Facial Action Temporal Dynamics

Author

Valstar, Michel F., Pantic, Maja, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lew, Michael, editor, Sebe, Nicu, editor, Huang, Thomas S., editor, and Bakker, Erwin M., editor

Published
2007
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170. Face for Ambient Interface

Author

Pantic, Maja, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Cai, Yang, editor, and Abascal, Julio, editor

Published
2006
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178. Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?

Author

Shukla, Abhinav, Petridis, Stavros, and Pantic, Maja

Abstract

Self-supervised learning has attracted plenty of recent research interest. However, most works for self-supervision in speech are typically unimodal and there has been limited work that studies the interaction between audio and visual modalities for cross-modal self-supervision. This article (1) investigates visual self-supervision via face reconstruction to guide the learning of audio representations; (2) proposes an audio-only self-supervision approach for speech representation learning; (3) shows that a multi-task combination of the proposed visual and audio self-supervision is beneficial for learning richer features that are more robust in noisy conditions; (4) shows that self-supervised pretraining can outperform fully supervised training and is especially useful to prevent overfitting on smaller sized datasets. We evaluate our learned audio representations for discrete emotion recognition, continuous affect recognition and automatic speech recognition. We outperform existing self-supervised methods for all tested downstream tasks. Our results demonstrate the potential of visual self-supervision for audio feature learning and suggest that joint visual and audio self-supervision leads to more informative audio representations for speech and emotion recognition. [ABSTRACT FROM AUTHOR]

Published
2023
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180. Evaluating the Impact of Voice Activity Detection on Speech Emotion Recognition for Autistic Children

Author

Milling, Manuel, primary, Baird, Alice, additional, Bartl-Pokorny, Katrin D., additional, Liu, Shuo, additional, Alcorn, Alyssa M., additional, Shen, Jie, additional, Tavassoli, Teresa, additional, Ainger, Eloise, additional, Pellicano, Elizabeth, additional, Pantic, Maja, additional, Cummins, Nicholas, additional, and Schuller, Björn W., additional

Published
2022
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188. Unobtrusive Deception Detection

Author

Calvo, Rafael, primary, D'Mello, Sidney, additional, Gratch, Jonathan, additional, Kappas, Arvid, additional, Elkins, Aaron, additional, Zafeiriou, Stefanos, additional, Pantic, Maja, additional, and Burgoon, Judee, additional

Published
2015
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189. Social Signal Processing

Author

Calvo, Rafael, primary, D'Mello, Sidney, additional, Gratch, Jonathan, additional, Kappas, Arvid, additional, Pantic, Maja, additional, and Vinciarelli, Alessandro, additional

Published
2015
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191. From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Author

Gunes, Hatice, Piccardi, Massimo, Pantic, Maja, and Or, Jimmy

Subjects
Facial expression, Modalities, business.industry, Computer science, Speech recognition, 05 social sciences, HMI-MI: MULTIMODAL INTERACTIONS, 050109 social psychology, 02 engineering and technology, Cognitive neuroscience, Affect (psychology), EC Grant Agreement nr.: FP7/211486, Motion (physics), EWI-14797, Duration (music), 0202 electrical engineering, electronic engineering, information engineering, IR-65266, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Noise (video), METIS-255077, Telecommunications, business, EC Grant Agreement nr.: FP6/0027787, Gesture
Abstract

Human affect sensing can be obtained from a broad range of behavioral cues and signals that are available via visual, acoustic, and tactual expressions or presentations of emotions. Affective states can thus be recognized from visible/external signals such as gestures (e.g., facial expressions, body gestures, head movements, etc.), and speech (e.g., parameters such as pitch, energy, frequency and duration), or invisible/internal signals such as physiological signals (e.g., heart rate, skin conductivity, salivation, etc.), brain and scalp signals, and thermal infrared imagery. Despite the available range of cues and modalities in human-human interaction (HHI), the mainstream emotion research has mostly focused on facial expressions (Hadjikhani & De Gelder, 2003). In line with this, most of the past research on affect sensing and recognition has also focused on facial expressions and on data that has been posed on demand or acquired in laboratory settings. Additionally, each sense such as vision, hearing, and touch has been considered in isolation. However, natural human-human interaction is multimodal and not occurring in predetermined, restricted and controlled settings. In the day-to-day world people do not present themselves to others as voiceor body-less faces or faceor body-less voices (Walker-Andrews, 1997). Moreover, the available emotional signals such as facial expression, head movement, hand gestures, and voice are unified in space and time (see Figure 1). They inherently share the same spatial location, and their occurrences are temporally synchronized. Cognitive neuroscience research thus claims that information coming from various modalities is combined in our brains to yield multimodally determined percepts (Driver & Spence, 2000). In real life situations, our different senses receive correlated information about the same external event. When assessing each others’ emotional or affective state, we are capable of handling significantly variable conditions in terms of viewpoint (i.e. frontal, profile, even back view), tilt angle, distance (i.e., face to face as well as at a distance) , illumination (i.e., both day and night conditions), occlusions (e.g., even when some body parts are occluded), motion (e.g., both when stationary and moving, walking and talking) and noise (e.g., while many people are chatting and interacting simultaneously).

Published
2021

194. End-to-End Video-to-Speech Synthesis Using Generative Adversarial Networks

Author

Mira, Rodrigo, Vougioukas, Konstantinos, Ma, Pingchuan, Petridis, Stavros, Schuller, Bjorn W., and Pantic, Maja

Abstract

Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an intermediate representation is inferred from the video and is then decoded into waveform audio using a vocoder or a waveform reconstruction algorithm. In this work, we propose a new end-to-end video-to-speech model based on generative adversarial networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm. Our model consists of an encoder–decoder architecture that receives raw video as input and generates speech, which is then fed to a waveform critic and a power critic. The use of an adversarial loss based on these two critics enables the direct synthesis of the raw audio waveform and ensures its realism. In addition, the use of our three comparative losses helps establish direct correspondence between the generated audio and the input video. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID, and that it is the first end-to-end model to produce intelligible speech for Lip Reading in the Wild (LRW), featuring hundreds of speakers recorded entirely “in the wild.” We evaluate the generated samples in two different scenarios—seen and unseen speakers—using four objective metrics which measure the quality and intelligibility of artificial speech. We demonstrate that the proposed approach outperforms all previous works in most metrics on GRID and LRW.

Published
2023
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195. LEARNING SEPARABLE TIME-FREQUENCY FILTERBANKS FOR AUDIO CLASSIFICATION

Author

Pu, Jie Panagakis, Yannis Pantic, Maja

Abstract

State-of-the-art audio classification systems often apply deep neural networks on hand-crafted features (e.g., spectrogram-based representations), instead of learning features directly from raw audio. Moreover, these audio networks have millions of unknown parameters need to be learned, which causes a great demand for computational resources and training data. In this paper, we aim to learn audio representations directly from raw audio, and at the same time mitigate its training burden by employing a light-weight architecture. In particular, we propose to learn separable filters, parametrized with only a few variables, namely center frequency and bandwidth, facilitating training and offering interpretability of learned representations. The generality of the proposed method is demonstrated by applying it onto two applications, namely 1) speaker identification and 2) acoustic event recognition. Experimental results indicate its effectiveness on these applications, especially when small amount of training data is available.

Published
2021

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