18 citations · 24 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models
Parul Gupta, Munawar Hayat, Abhinav Dhall +1
Few-shot image synthesis entails generating diverse and realistic images of novel categories using only a few example images. While multiple recent efforts in this direction have a…
DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition
Parul Gupta, Tuan Nguyen, Abhinav Dhall +3
The task of Visual Relationship Recognition (VRR) aims to identify relationships between two interacting objects in an image and is particularly challenging due to the widely-sprea…
Erasing Undesirable Influence in Diffusion Models
Jing Wu, Trung Le, Munawar Hayat +1
Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. Although various t…
AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
Zhixi Cai, Shreya Ghosh, Aman Pankaj Adatia +4
The detection and localization of highly realistic deepfake audio-visual content are challenging even for the most advanced state-of-the-art methods. While most of the research eff…
Survey: Image Mixing and Deleting for Data Augmentation
Humza Naveed, Saeed Anwar, Munawar Hayat +2
Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting and enhance their generalization and performance, various methods have been suggested i…