57 citations · 70 across the 10 of their papers we have counts for
11 papers
Mood as a Contextual Cue for Improved Emotion Inference
Soujanya Narayana, Ibrahim Radwan, Ramanathan Subramanian +1
Psychological studies observe that emotions are rarely expressed in isolation and are typically influenced by the surrounding context. While recent studies effectively harness uni-…
Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
Ravikiran Parameshwara, Ibrahim Radwan, Akshay Asthana +3
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…
A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference
Soujanya Narayana, Ibrahim Radwan, Ravikiran Parameshwara +4
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less…
Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention
Soujanya Narayana, Ramanathan Subramanian, Ibrahim Radwan +1
While emotion and mood interchangeably used, they differ in terms of duration, intensity and attributes. Even as multiple psychology studies examine the mood-emotion relationship,…
To Improve Is to Change: Towards Improving Mood Prediction by Learning Changes in Emotion
Soujanya Narayana, Ramanathan Subramanian, Ibrahim Radwan +1
Although the terms mood and emotion are closely related and often used interchangeably, they are distinguished based on their duration, intensity and attribution. To date, hardly a…
Visual Attention Methods in Deep Learning: An In-Depth Survey
Mohammed Hassanin, Saeed Anwar, Ibrahim Radwan +2
Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more o…
CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation
Mohammed Hassanin, Abdelwahed Khamiss, Mohammed Bennamoun +2
3D human pose estimation can be handled by encoding the geometric dependencies between the body parts and enforcing the kinematic constraints. Recently, Transformer has been adopte…
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals
Ravikiran Parameshwara, Soujanya Narayana, Murugappan Murugappan +3
While Parkinson's disease (PD) is typically characterized by motor disorder, there is evidence of diminished emotion perception in PD patients. This study examines the utility of a…
Learning Discriminative Representations for Multi-Label Image Recognition
Mohammed Hassanin, Ibrahim Radwan, Salman Khan +1
Multi-label recognition is a fundamental, and yet is a challenging task in computer vision. Recently, deep learning models have achieved great progress towards learning discriminat…
Mitigating the Impact of Adversarial Attacks in Very Deep Networks
Mohammed Hassanin, Ibrahim Radwan, Nour Moustafa +2
Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poi…
Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees
Ibrahim Radwan, Abhinav Dhall, Roland Goecke
In this paper, we study the problem of learning a model for human pose estimation as mixtures of compositional sub-trees in two layers of prediction. This involves estimating the p…