Search Results for author: Howard Zhou

Found 4 papers, 2 papers with code

IBRNet: Learning Multi-View Image-Based Rendering

1 code implementation CVPR 2021 Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser

Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.

Neural Rendering Novel View Synthesis

Blockout: Dynamic Model Selection for Hierarchical Deep Networks

no code implementations CVPR 2016 Calvin Murdock, Zhen Li, Howard Zhou, Tom Duerig

Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model.

General Classification Image Classification +1

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

1 code implementation20 Nov 2015 Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.

Active Learning

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