Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Cardinal, Patrick"'
In video-based emotion recognition (ER), it is important to effectively leverage the complementary relationship among audio (A) and visual (V) modalities, while retaining the intra-modal characteristics of individual modalities. In this paper, a recu
Externí odkaz:
http://arxiv.org/abs/2304.07958
Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention
Automatic emotion recognition (ER) has recently gained lot of interest due to its potential in many real-world applications. In this context, multimodal approaches have been shown to improve performance (over unimodal approaches) by combining diverse
Externí odkaz:
http://arxiv.org/abs/2209.09068
This paper introduces a new synthesis-based defense algorithm for counteracting with a varieties of adversarial attacks developed for challenging the performance of the cutting-edge speech-to-text transcription systems. Our algorithm implements a Sob
Externí odkaz:
http://arxiv.org/abs/2207.06858
Autor:
Esmaeilpour, Mohammad, Chaalia, Nourhene, Abusitta, Adel, Devailly, Francois-Xavier, Maazoun, Wissem, Cardinal, Patrick
This paper introduces a novel generative adversarial network (GAN) for synthesizing large-scale tabular databases which contain various features such as continuous, discrete, and binary. Technically, our GAN belongs to the category of class-condition
Externí odkaz:
http://arxiv.org/abs/2205.11693
Data anonymization is often a task carried out by humans. Automating it would reduce the cost and time required to complete this task. This paper presents a pipeline to automate the anonymization of audio data in French. We propose a pipeline, which
Externí odkaz:
http://arxiv.org/abs/2204.12622
This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network, namely ResNet-18. Our main
Externí odkaz:
http://arxiv.org/abs/2204.07018
Autor:
Praveen, R. Gnana, de Melo, Wheidima Carneiro, Ullah, Nasib, Aslam, Haseeb, Zeeshan, Osama, Denorme, Théo, Pedersoli, Marco, Koerich, Alessandro, Bacon, Simon, Cardinal, Patrick, Granger, Eric
Multimodal emotion recognition has recently gained much attention since it can leverage diverse and complementary relationships over multiple modalities (e.g., audio, visual, biosignals, etc.), and can provide some robustness to noisy modalities. Mos
Externí odkaz:
http://arxiv.org/abs/2203.14779
Autor:
Esmaeilpour, Mohammad, Chaalia, Nourhene, Abusitta, Adel, Devailly, Francois-Xavier, Maazoun, Wissem, Cardinal, Patrick
This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns. Our proposed approach employs an adapted preprocessing scheme and a novel conditional term for the generator network t
Externí odkaz:
http://arxiv.org/abs/2111.06549
Multimodal analysis has recently drawn much interest in affective computing, since it can improve the overall accuracy of emotion recognition over isolated uni-modal approaches. The most effective techniques for multimodal emotion recognition efficie
Externí odkaz:
http://arxiv.org/abs/2111.05222
Publikováno v:
IEEE Signal Processing Letters (2021) 1-5
This paper proposes a new defense approach for counteracting state-of-the-art white and black-box adversarial attack algorithms. Our approach fits into the implicit reactive defense algorithm category since it does not directly manipulate the potenti
Externí odkaz:
http://arxiv.org/abs/2103.14717