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Found 12 papers, 3 papers with code
Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases.
Explainable Depression Detection via Head Motion Patterns
While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker.
A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention.
Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits.
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals
Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals.
Characterizing Hirability via Personality and Behavior
Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP).
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Ranked #7 on
Adversarial Defense
on CIFAR-10
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction
This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge.
Joint Registration and Representation Learning for Unconstrained Face Identification
We validate the proposed scheme on template based unconstrained face identification.
Analyzing the Affect of a Group of People Using Multi-modal Framework
We fuse face, upperbody and scene information for robustness of GER against the challenging environments.
Harnessing the Deep Net Object Models for Enhancing Human Action Recognition
In this study, the influence of objects is investigated in the scenario of human action recognition with large number of classes.
Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees
The proposed method handles occlusions during the inference process by identifying overlapping regions between different sub-trees and introducing a penalty term for overlapping parts.
