3 citations · 11 across the 30 of their papers we have counts for
34 papers
When, Where, and Why: Hierarchical Causal Explainability for Video Face Understanding with MLLMs
Anubhav Kataria, Shreya Ghosh, Jingwen Ye +1
Understanding facial behaviour in video requires answering three fundamental questions: when a key facial expression occurs, where the model attends on the face at that moment, and…
Explainable Deepfake Detection Challenge
Abhijeet Narang, Kartik Kuckreja, Shreya Ghosh +4
Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is importan…
DREAMS: Diverse Reactions of Engagement and Attention Mind States Dataset
Monisha Singh, Gulshan Sharma, Ximi Hoque +1
Active attention and engagement are important in improving users' learning experiences. Engagement refers to the level of involvement and interest individuals show towards a partic…
XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection
Abhijeet Narang, Kartik Kuckreja, Shreya Ghosh +3
As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limiting reliability and user tr…
Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision
Kartik Kuckreja, Parul Gupta, Muhammad Haris Khan +1
Deepfake detection models often generate natural-language explanations, yet their reasoning is frequently ungrounded in visual evidence, limiting reliability. Existing evaluations…
VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping
Sanoojan Baliah, Yohan Abeysinghe, Rusiru Thushara +4
We present a training-free, plug-and-play method, namely VFace, for high-quality face swapping in videos. It can be seamlessly integrated with image-based face swapping approaches…
Investigating the Viability of Employing Multi-modal Large Language Models in the Context of Audio Deepfake Detection
Akanksha Chuchra, Shukesh Reddy, Sudeepta Mishra +2
While Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) have shown strong generalisation in detecting image and video deepfakes, their use for audio deepfa…
A Survey of Body and Face Motion: Datasets, Performance Evaluation Metrics and Generative Techniques
Lownish Rai Sookha, Nikhil Pakhale, Mudasir Ganaie +1
Body and face motion play an integral role in communication. They convey crucial information on the participants. Advances in generative modeling and multi-modal learning have enab…
CSGaze: Context-aware Social Gaze Prediction
Surbhi Madan, Shreya Ghosh, Ramanathan Subramanian +2
A person's gaze offers valuable insights into their focus of attention, level of social engagement, and confidence. In this work, we investigate how contextual cues combined with v…
A Multi-Modal Neuro-Symbolic Approach for Spatial Reasoning-Based Visual Grounding in Robotics
Simindokht Jahangard, Mehrzad Mohammadi, Abhinav Dhall +1
Visual reasoning, particularly spatial reasoning, is a challenging cognitive task that requires understanding object relationships and their interactions within complex environment…
SFANet: Spatial-Frequency Attention Network for Deepfake Detection
Vrushank Ahire, Aniruddh Muley, Shivam Zample +4
Detecting manipulated media has now become a pressing issue with the recent rise of deepfakes. Most existing approaches fail to generalize across diverse datasets and generation te…
SynchroRaMa : Lip-Synchronized and Emotion-Aware Talking Face Generation via Multi-Modal Emotion Embedding
Phyo Thet Yee, Dimitrios Kollias, Sudeepta Mishra +1
Audio-driven talking face generation has received growing interest, particularly for applications requiring expressive and natural human-avatar interaction. However, most existing…
PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Ganesh Krishnasamy +2
Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is a fundamental task in many computational vision problems. Numerous data-driven methods have been proposed t…
LayLens: Improving Deepfake Understanding through Simplified Explanations
Abhijeet Narang, Parul Gupta, Liuyijia Su +1
This demonstration paper presents , a tool aimed to make deepfake understanding easier for users of all educational backgrounds. While prior works often rely on o…
AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations
Zhixi Cai, Kartik Kuckreja, Shreya Ghosh +5
The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in…
Improving Personalized Image Generation through Social Context Feedback
Parul Gupta, Abhinav Dhall, Thanh-Toan Do
Personalized image generation, where reference images of one or more subjects are used to generate their image according to a scene description, has gathered significant interest i…
Gems: Group Emotion Profiling Through Multimodal Situational Understanding
Anubhav Kataria, Surbhi Madan, Shreya Ghosh +2
Understanding individual, group and event level emotions along with contextual information is crucial for analyzing a multi-person social situation. To achieve this, we frame emoti…
Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations
Parul Gupta, Shreya Ghosh, Tom Gedeon +2
The rapid advancement of GenAI technology over the past few years has significantly contributed towards highly realistic deepfake content generation. Despite ongoing efforts, the r…
Tell me Habibi, is it Real or Fake?
Kartik Kuckreja, Parul Gupta, Injy Hamed +3
Deepfake generation methods are evolving fast, making fake media harder to detect and raising serious societal concerns. Most deepfake detection and dataset creation research focus…
MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network
Vrushank Ahire, Kunal Shah, Mudasir Nazir Khan +4
Dynamic emotion recognition in the wild remains challenging due to the transient nature of emotional expressions and temporal misalignment of multi-modal cues. Traditional approach…
A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Lemuel Lai En Che +3
Reconstruction of High Dynamic Range (HDR) from Low Dynamic Range (LDR) images is an important computer vision task. There is a significant amount of research utilizing both conven…
MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing
Shreya Ghosh, Zhixi Cai, Abhinav Dhall +3
With the rapid advancements in multimodal generative technology, Affective Computing research has provoked discussion about the potential consequences of AI systems equipped with e…
1M-Deepfakes Detection Challenge
Zhixi Cai, Abhinav Dhall, Shreya Ghosh +4
The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of dig…
MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context Understanding
Surbhi Madan, Shreya Ghosh, Lownish Rai Sookha +4
Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causal…