Richard Strong Bowen — accepted AI-conference papers

2026

Triangle Multiplication is All You Need for Biomolecular Structure Representations

Jeffrey Ouyang-Zhang, Pranav Murugan, Daniel Jesus Diaz, Gianluca Scarpellini, Richard Strong Bowen, Nate Gruver +4

ICLR 2026poster

AlphaFold has transformed protein structure prediction, but emerging applications such as virtual ligand screening, proteome-wide folding, and de novo binder design demand predictions at a massive scale, where runtime and memory costs become prohibitive. A major bottleneck lies in the Pairformer bac…

Cited by 0 Source ↗code ↗Scholar ↗

2021

Deep Survival Analysis With Longitudinal X-Rays for COVID-19

Michelle Shu, Richard Strong Bowen, Charles Herrmann, Gengmo Qi, Michele Santacatterina, Ramin Zabih

ICCV 2021poster

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 incorporate images due to their high dimensionality. We propose a deep learning approach that naturally incorporates multiple,…

Cited by 13 PDF ↗Scholar ↗

2021

OCONet: Image Extrapolation by Object Completion

Richard Strong Bowen, Huiwen Chang, Charles Herrmann, Piotr Teterwak, Ce Liu, Ramin Zabih

CVPR 2021poster

Image extrapolation extends an input image beyond the originally-captured field of view. Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes. We introduce OCONet…

Cited by 21 PDF ↗Scholar ↗

2020

Channel selection using Gumbel Softmax

Charles Herrmann, Richard Strong Bowen, Ramin Zabih

ECCV 2020poster

Important applications such as mobile computing require reducing the computational costs of neural network inference. Ideally, applications would specify their preferred tradeoff between accuracy and speed, and the network would optimize this end-to-end, using classification error to remove parts of…

Cited by 57 Source ↗PDF ↗code ↗Scholar ↗

2020

Learning to Autofocus

Charles Herrmann, Richard Strong Bowen, Neal Wadhwa, Rahul Garg, Qiurui He, Jonathan T. Barron +1

CVPR 2020poster

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 realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi…

Cited by 68 PDF ↗Scholar ↗

2018

Object-centered image stitching

Charles Herrmann, Chen Wang, Richard Strong Bowen, Emil Keyder, Ramin Zabih

ECCV 2018poster

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 pixel the source image it should come from; and blending, which smooths transitions over the seams. As described in Szeli…

Cited by 45 Source ↗PDF ↗Scholar ↗

2018

Robust image stitching with multiple registrations

Charles Herrmann, Chen Wang, Richard Strong Bowen, Emil Keyder, Michael Krainin, Ce Liu +1

ECCV 2018poster

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 consumers in smartphones and other cameras. Traditionally, the problem is decomposed into three phases: registration, wh…

Cited by 77 Source ↗PDF ↗Scholar ↗