3 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…