# Scalable Annotation of Fine-Grained Categories Without Experts

arXiv: https://arxiv.org/abs/1709.02482 · HTML: https://mlsanity.com/paper/1709.02482/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [BARACK: Partially Supervised Group Robustness With Guarantees](https://mlsanity.com/paper/2201.00072/) (2021)
- [Federated Fairness without Access to Sensitive Groups](https://mlsanity.com/paper/2402.14929/) (2024)

## Abstract (arXiv)

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.