Search Results for author:
Found 29 papers, 6 papers with code
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities
The study of human emotions, traditionally a cornerstone in fields like psychology and neuroscience, has been profoundly impacted by the advent of artificial intelligence (AI).
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
Facial expression recognition is a pivotal component in machine learning, facilitating various applications.
Methods for Generating Drift in Text Streams
To learn from textual data over time, the machine learning system must account for concept drift.
Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products.
Joint Multimodal Transformer for Emotion Recognition in the Wild
Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e. g., visual, textual, physiological, and auditory modalities.
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
In particular, using this aus codebook, input image expression label, and facial landmarks, a single action units heatmap is built to indicate the most discriminative regions of interest in the image w. r. t the facial expression.
Facial Expression Recognition
Facial Expression Recognition (FER)
Concept Drift Adaptation in Text Stream Mining Settings: A Comprehensive Review
Due to the advent and increase in the popularity of the Internet, people have been producing and disseminating textual data in several ways, such as reviews, social media posts, and news articles.
Large-Margin Representation Learning for Texture Classification
The core of such an approach is a loss function that computes the distances between instances of interest and support vectors.
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks
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.
Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform
Texture descriptors have been quite popular in medical image analysis, particularly in histopathologic images (HI), due to the variability of both the texture found in such images and the tissue appearance due to irregularity in the staining process.
Towards Robust Speech-to-Text Adversarial Attack
This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo.
Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems
This paper introduces a defense approach against end-to-end adversarial attacks developed for cutting-edge speech-to-text systems.
A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures
Texture can be defined as the change of image intensity that forms repetitive patterns, resulting from physical properties of the object's roughness or differences in a reflection on the surface.
Machine Learning Methods for Histopathological Image Analysis: A Review
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis.
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
In this paper, we investigate the suitability of state-of-the-art deep learning architectures based on convolutional neural networks (CNNs) for continuous emotion recognition using long video sequences captured in-the-wild.
Class-Conditional Defense GAN Against End-to-End Speech Attacks
In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo.
Conditioning Trick for Training Stable GANs
In this paper we propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training.
Improving Stability of LS-GANs for Audio and Speech Signals
In this paper we address the instability issue of generative adversarial network (GAN) by proposing a new similarity metric in unitary space of Schur decomposition for 2D representations of audio and speech signals.
From Sound Representation to Model Robustness
In this paper, we investigate the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network.
Detection of Adversarial Attacks and Characterization of Adversarial Subspace
Adversarial attacks have always been a serious threat for any data-driven model.
Cross-Representation Transferability of Adversarial Attacks: From Spectrograms to Audio Waveforms
Furthermore, the audio waveforms reconstructed from the perturbed spectrograms are also able to fool a 1D CNN trained on the original audio.
Incremental and Decremental Fuzzy Bounded Twin Support Vector Machine
In this paper we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and learning from data streams.
Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents
This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning.
Bag-of-Audio-Words based on Autoencoder Codebook for Continuous Emotion Prediction
The conventional BoW model is based on a dictionary (codebook) built from elementary representations which are selected randomly or by using a clustering algorithm on a training dataset.
Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods
Automatic measuring of speaker sincerity degree is a novel research problem in computational paralinguistics.
A Novel Orthogonal Direction Mesh Adaptive Direct Search Approach for SVM Hyperparameter Tuning
In this paper, we propose the use of a black-box optimization method called deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal directions (Ortho-MADS) for the selection of hyperparameters of Support Vector Machines with a Gaussian kernel.
A Robust Approach for Securing Audio Classification Against Adversarial Attacks
In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of the most common machine learning model, namely deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform (STFT), discrete wavelet transform (DWT) and cross recurrent plot (CRP) against several state-of-the-art adversarial attacks.
End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network
In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal.
Ranked #6 on
Environmental Sound Classification
on UrbanSound8K
(Accuracy metric, using extra
training data)
Environmental Sound Classification
General Classification
+1
Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical $K$-Means++ algorithm and a new architecture for high-level data augmentation.
