activity
20162021
collaborators

8 papers

eess.IV2021

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…

cs.CV2021

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…

cs.CV2020

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…

cs.CV2020

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…

cs.CV2020

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…

cs.CV2018

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…