This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER).
Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales.
Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet.
The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.
Automatic plant classification is a challenging problem due to the wide biodiversity of the existing plant species in a fine-grained scenario.
In this paper, we present a new model for continuous emotion recognition based on facial expression recognition by using an unsupervised learning approach based on transfer learning and autoencoders.
Such a problem is troublesome because most of the ML algorithms attempt to optimize a loss function that does not take into account the data imbalance.
In this paper, we present a new method for multi-dimensional data classification that relies on two premises: 1) multi-dimensional data are usually represented by tensors, since this brings benefits from multilinear algebra and established tensor factorization methods; and 2) multilinear data can be described by a subspace of a vector space.
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios.
This paper proposes a face anti-spoofing user-centered model (FAS-UCM).
This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition.
In this paper we propose a fusion approach to continuous emotion recognition that combines visual and auditory modalities in their representation spaces to predict the arousal and valence levels.
This paper presents a novel approach for image retrieval and pattern spotting in document image collections.
A common way to circumvent the lack of data is to use CNNs trained on large datasets of different domains and fine-tuning the layers of such networks to the target domain.
In this paper, the concept of representation learning based on deep neural networks is applied as an alternative to the use of handcrafted features in a method for automatic visual inspection of corroded thermoelectric metallic pipes.
In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods.
This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-trained with ImageNet dataset.
For such an aim, corner points are extracted from groups of people in a foreground image and computed by a learning algorithm which estimates the number of people in the scene.