other categories as well. To further supplement our dataset we also searched for scene/object category pairs, see [the appendix]{}. We downloaded at most 5 photos taken by a single photographer within a short time window. In the rare cases in which enough images could not be found, we searched for single categories and performed an explicit filtering stage to remove iconic images. The result is a collection of 328,000 images with rich contextual relationships between objects as shown in Figs. \[fig:iconic\](c) and \[fig:exampleimages\].

Image Annotation
================

[0.47]{} ![image](figs/expert-worker){width="\textwidth"} \[fig:recall\]

[0.50]{} ![image](figs/worker_all){width="\textwidth"} \[fig:all\_worker\]

We next describe how we annotated our image collection. Due to our desire to label over 2.5 million object instances, the design of a cost efficient yet high quality annotation pipeline was critical. The annotation pipeline is outlined in Fig. \[fig:pipeline\]. For all crowdsourcing tasks we used workers on Amazon’s Mechanical Turk (AMT). Our user interfaces are described in detail in [the appendix]{}. Note that, since the original version of this work [@eccv], we have taken a number of steps to further improve the quality of the annotations. In particular, we have increased the number of annotators for the category labeling and instance spotting stages to eight. We 