27 citations · 34 across the 11 of their papers we have counts for
12 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…
Neural Encoding of Songs is Modulated by Their Enjoyment
Gulshan Sharma, Pankaj Pandey, Ramanathan Subramanian +2
We examine user and song identification from neural (EEG) signals. Owing to perceptual subjectivity in human-media interaction, music identification from brain signals is a challen…
Affective Computational Advertising Based on Perceptual Metrics
Soujanya Narayana, Shweta Jain, Harish Katti +2
We present \textbf{ACAD}, an \textbf{a}ffective \textbf{c}omputational \textbf{ad}vertising framework expressly derived from perceptual metrics. Different from advertising methods…
Expert and Crowd-Guided Affect Annotation and Prediction
Ramanathan Subramanian, Yan Yan, Nicu Sebe
We employ crowdsourcing to acquire time-continuous affective annotations for movie clips, and refine noisy models trained from these crowd annotations incorporating expert informat…
FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios
Vineet Mehta, Parul Gupta, Ramanathan Subramanian +1
This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media. FakeBuster is a standalone deep lear…
GAZED- Gaze-guided Cinematic Editing of Wide-Angle Monocular Video Recordings
K L Bhanu Moorthy, Moneish Kumar, Ramanathan Subramaniam +1
We present GAZED- eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational…
The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception
Parul Gupta, Komal Chugh, Abhinav Dhall +1
We present \textbf{FakeET}-- an eye-tracking database to understand human visual perception of \emph{deepfake} videos. Given that the principal purpose of deepfakes is to deceive h…
Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
Komal Chugh, Parul Gupta, Abhinav Dhall +1
We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manip…