of all object categories not later rejected as false positives are detected given 8 annotators. Note that a similar analysis may be done for instance spotting in which 8 annotators were also used.

Finally, Fig. \[fig:workers\](b) re-examines precision and recall of AMT workers on category labeling on a much larger set of images. The number of workers (circle size) and average number of jobs per worker (circle color) is shown for each precision/recall range. Unlike in Fig. \[fig:workers\](a), we used a leave-one-out evaluation procedure where a category was considered present if *any* of the remaining workers named the category. Therefore, overall worker precision is substantially higher. Workers who completed the most jobs also have the highest precision; all jobs from workers below the black line were rejected.

Caption Annotation
------------------

We added five written caption descriptions to each image in [MS COCO]{}. A full description of the caption statistics and how they were gathered will be provided shortly in a separate publication.

Dataset Statistics
==================

Next, we analyze the properties of the Microsoft Common Objects in COntext ([MS COCO]{}) dataset in comparison to several other popular datasets. These include ImageNet [@Imagenet], PASCAL VOC 2012 [@PASCAL], and SUN [@SUN]. Each of 