• Publications
  • Influence
Predicting Ground-Level Scene Layout from Aerial Imagery
TLDR
A novel strategy for learning to extract semantically meaningful features from aerial imagery is introduced to predict (noisy) semantic features automatically extracted from co-located ground imagery and it is demonstrated that by finetuning this model, it can achieve more accurate semantic segmentation than two baseline initialization strategies. Expand
Quantifying curb appeal
TLDR
This work proposes to use street-level imagery of a home, in addition to the objective attributes, to estimate the price of the home, thereby quantifying curb appeal and finds that using images and objective attributes together results in more accurate home price estimates than using either in isolation. Expand
Inconsistent Performance of Deep Learning Models on Mammogram Classification.
TLDR
The results demonstrate performance inconsistency across the data sets and models, indicating that the high performance of deep learning models on one data set cannot be readily transferred to unseen external data sets, and these models need further assessment and validation before being applied in clinical practice. Expand
A Generative Model of Worldwide Facial Appearance
TLDR
This work proposes GPS2Face, a dual-component generative network architecture that enables flexible facial generation with fine-grained control of latent factors and uses facial landmarks as a guide to synthesize likely faces for locations around in the world. Expand
Localization of Drosophila embryos using connected components in scale space
TLDR
A localization framework based on the analysis of connected components in the Gaussian scale space of an embryonic image is introduced and three criteria for the selection of the optimal scale are proposed. Expand
Who goes there?: approaches to mapping facial appearance diversity
TLDR
This work proposes learning generative models that relate facial appearance and geographic location, and describes a framework for constructing a web-based visualization that captures the geospatial distribution of human facial appearance. Expand
Learning scale ranges for the extraction of regions of interest
TLDR
This work introduces an alternative scheme that aims to learn scale ranges from training images in order to reduce the search space and thus computational costs in the context of the extraction of Regions of Interest. Expand
Modeling and Mapping Location-Dependent Human Appearance
TLDR
The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by the framework, and several generative and discriminative models that use the dataset to learn the relationship between human appearance, location, and time. Expand