10 citations · 23 across the 10 of their papers we have counts for
4 papers · 1 filter
VADER: Video Alignment Differencing and Retrieval
Alexander Black, Simon Jenni, Tu Bui +5
We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely alig…
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
Trisha Mittal, Ritwik Sinha, Viswanathan Swaminathan +2
As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is incre…
Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention
Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli +2
We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our m…
HighlightMe: Detecting Highlights from Human-Centric Videos
Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli +2
We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multipl…