5 citations · 13 across the 10 of their papers we have counts for
4 papers · 1 filter
Dimensions of Motion: Monocular Prediction through Flow Subspaces
Richard Strong Bowen, Richard Tucker, Ramin Zabih +1
We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompa…
Pyramid Adversarial Training Improves ViT Performance
Charles Herrmann, Kyle Sargent, Lu Jiang +5
Aggressive data augmentation is a key component of the strong generalization capabilities of Vision Transformer (ViT). One such data augmentation technique is adversarial training…
Deep survival analysis with longitudinal X-rays for COVID-19
Michelle Shu, Richard Strong Bowen, Charles Herrmann +3
Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds. However, classical techniques like the Cox model cannot directly incorpo…
AutoFlow: Learning a Better Training Set for Optical Flow
Deqing Sun, Daniel Vlasic, Charles Herrmann +6
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the pro…