62 citations · 176 across the 28 of their papers we have counts for
6 papers · 1 filter
Iterative Text-based Editing of Talking-heads Using Neural Retargeting
Xinwei Yao, Ohad Fried, Kayvon Fatahalian +1
We present a text-based tool for editing talking-head video that enables an iterative editing workflow. On each iteration users can edit the wording of the speech, further refine m…
Background Splitting: Finding Rare Classes in a Sea of Background
Ravi Teja Mullapudi, Fait Poms, William R. Mark +2
We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to t…
Analyzing Who and What Appears in a Decade of US Cable TV News
James Hong, Will Crichton, Haotian Zhang +9
Cable TV news reaches millions of U.S. households each day, meaning that decisions about who appears on the news and what stories get covered can profoundly influence public opinio…
Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players
Haotian Zhang, Cristobal Sciutto, Maneesh Agrawala +1
We present a system that converts annotated broadcast video of tennis matches into interactively controllable video sprites that behave and appear like professional tennis players.…
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Mayee F. Chen, Daniel Y. Fu, Frederic Sala +5
Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We…
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Daniel Y. Fu, Mayee F. Chen, Frederic Sala +3
Weak supervision is a popular method for building machine learning models without relying on ground truth annotations. Instead, it generates probabilistic training labels by estima…