61 citations · 119 across the 5 of their papers we have counts for
6 papers
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
Paul Upchurch, Ransen Niu
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.) to each pixel. We find that a model trained on existing data underp…
Block Annotation: Better Image Annotation for Semantic Segmentation with Sub-Image Decomposition
Hubert Lin, Paul Upchurch, Kavita Bala
Image datasets with high-quality pixel-level annotations are valuable for semantic segmentation: labelling every pixel in an image ensures that rare classes and small objects are a…
Deep Feature Interpolation for Image Content Changes
Paul Upchurch, Jacob Gardner, Geoff Pleiss +4
We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, it relies only on simple linear in…
From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators
Paul Upchurch, Noah Snavely, Kavita Bala
We propose a new neural network architecture for solving single-image analogies - the generation of an entire set of stylistically similar images from just a single input image. So…
Deep Manifold Traversal: Changing Labels with Convolutional Features
Jacob R. Gardner, Paul Upchurch, Matt J. Kusner +4
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in or…
Material Recognition in the Wild with the Materials in Context Database
Sean Bell, Paul Upchurch, Noah Snavely +1
Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make…