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—This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative parts. To this end, we propose an object representation that detects important parts and describes(More)
The United States spends more than $1B each year on initiatives such as the Amer-ican Community Survey (ACS), a labor-intensive door-to-door study that measures demographic factors 1. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous(More)
Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and(More)
We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts. In animals, there is a direct link between taxonomy and visual similarity: e.g. a collie (type of dog) looks more similar to other collies (e.g. smooth collie) than a greyhound (another type of dog). However, in synthetic(More)
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