[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Deep metric learning: A survey
M Kaya, HŞ Bilge - Symmetry, 2019 - mdpi.com
Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …
distance metric for learning tasks. Metric learning methods, which generally use a linear …
Facial expression recognition in the wild via deep attentive center loss
AH Farzaneh, X Qi - Proceedings of the IEEE/CVF winter …, 2021 - openaccess.thecvf.com
Learning discriminative features for Facial Expression Recognition (FER) in the wild using
Convolutional Neural Networks (CNNs) is a non-trivial task due to the significant intra-class …
Convolutional Neural Networks (CNNs) is a non-trivial task due to the significant intra-class …
Learning deep global multi-scale and local attention features for facial expression recognition in the wild
Facial expression recognition (FER) in the wild received broad concerns in which occlusion
and pose variation are two key issues. This paper proposed a global multi-scale and local …
and pose variation are two key issues. This paper proposed a global multi-scale and local …
Deep facial expression recognition: A survey
With the transition of facial expression recognition (FER) from laboratory-controlled to
challenging in-the-wild conditions and the recent success of deep learning techniques in …
challenging in-the-wild conditions and the recent success of deep learning techniques in …
Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
In this work, we present a framework to measure and mitigate intrinsic biases with respect to
protected variables-such as gender-in visual recognition tasks. We show that trained models …
protected variables-such as gender-in visual recognition tasks. We show that trained models …
Ad-corre: Adaptive correlation-based loss for facial expression recognition in the wild
Automated Facial Expression Recognition (FER) in the wild using deep neural networks is
still challenging due to intra-class variations and inter-class similarities in facial images …
still challenging due to intra-class variations and inter-class similarities in facial images …
Local learning with deep and handcrafted features for facial expression recognition
We present an approach that combines automatic features learned by convolutional neural
networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) …
networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) …
Facial expression recognition using local gravitational force descriptor-based deep convolution neural networks
An image is worth a thousand words; hence, a face image illustrates extensive details about
the specification, gender, age, and emotional states of mind. Facial expressions play an …
the specification, gender, age, and emotional states of mind. Facial expressions play an …
Head fusion: Improving the accuracy and robustness of speech emotion recognition on the IEMOCAP and RAVDESS dataset
Speech Emotion Recognition (SER) refers to the use of machines to recognize the emotions
of a speaker from his (or her) speech. SER benefits Human-Computer Interaction (HCI). But …
of a speaker from his (or her) speech. SER benefits Human-Computer Interaction (HCI). But …