5 citations · 13 across the 10 of their papers we have counts for
11 papers
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…
Object-centered image stitching
Charles Herrmann, Chen Wang, Richard Strong Bowen +2
Image stitching is typically decomposed into three phases: registration, which aligns the source images with a common target image; seam finding, which determines for each target p…
Robust image stitching with multiple registrations
Charles Herrmann, Chen Wang, Richard Strong Bowen +4
Panorama creation is one of the most widely deployed techniques in computer vision. In addition to industry applications such as Google Street View, it is also used by millions of…
Learning to Autofocus
Charles Herrmann, Richard Strong Bowen, Neal Wadhwa +4
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a reali…
Channel selection using Gumbel Softmax
Charles Herrmann, Richard Strong Bowen, Ramin Zabih
Important applications such as mobile computing require reducing the computational costs of neural network inference. Ideally, applications would specify their preferred tradeoff b…
A discriminative view of MRF pre-processing algorithms
Chen Wang, Charles Herrmann, Ramin Zabih
While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem. MRF inference can be accelerated by pre-processing techniq…
Some medical applications of example-based super-resolution
Ramin Zabih
Example-based super-resolution (EBSR) reconstructs a high-resolution image from a low-resolution image, given a training set of high-resolution images. In this note I propose some…
Structured learning of sum-of-submodular higher order energy functions
Alexander Fix, Thorsten Joachims, Sam Park +1
Submodular functions can be exactly minimized in polynomial time, and the special case that graph cuts solve with max flow \cite{KZ:PAMI04} has had significant impact in computer v…