Scalable Annotation of Fine-Grained Categories Without Experts
| Autor: | Timnit Gebru, Li Fei-Fei, Jonathan Krause, Jia Deng |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Information retrieval Computer science Collie business.industry Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Computer Science - Human-Computer Interaction Computer Science - Computer Vision and Pattern Recognition Smooth Collie 020207 software engineering 02 engineering and technology Crowdsourcing Human-Computer Interaction (cs.HC) World Wide Web Annotation Scalability 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences business 050107 human factors |
| Zdroj: | CHI |
| DOI: | 10.1145/3025453.3025930 |
| Popis: | 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 categories such as cars, objects with similar taxonomy can have very different appearance: e.g. a 2011 Ford F-150 Supercrew-HD looks the same as a 2011 Ford F-150 Supercrew-LL but very different from a 2011 Ford F-150 Supercrew-SVT. We introduce a graph based crowdsourcing algorithm to automatically group visually indistinguishable objects together. Using our workflow, we label 712,430 images by ~1,000 Amazon Mechanical Turk workers; resulting in the largest fine-grained visual dataset reported to date with 2,657 categories of cars annotated at 1/20th the cost of hiring experts. Comment: CHI 2017 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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