858 citations
- Indian Institute of Technology MadrasIN15 papers
- Indian Institute of Technology BombayIN11 papers
- Tata Institute of Fundamental ResearchIN11 papers
- Homi Bhabha National InstituteIN10 papers
- Indian Institute of Technology KharagpurIN9 papers
- Bhabha Atomic Research CentreIN8 papers
- University of LucknowIN8 papers
- Weizmann Institute of ScienceIL8 papers
- Centre National de la Recherche ScientifiqueFR7 papers
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16 papers · 1 filter
MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings
Surbhi Madan, Rishabh Jain, Gulshan Sharma +2
Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems. Furthermore, behavioral l…
Understanding Calibration of Deep Neural Networks for Medical Image Classification
Abhishek Singh Sambyal, Usma Niyaz, Narayanan C. Krishnan +1
In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play…
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs
Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan
Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining…
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…