---
author:
- |
    Tsung-Yi Lin Michael Maire Serge Belongie Lubomir Bourdev Ross Girshick\
    James Hays Pietro Perona Deva Ramanan C. Lawrence Zitnick Piotr Dollár
bibliography:
- 'coco.bib'
title: 'Microsoft COCO: Common Objects in Context'
---

Introduction
============

One of the primary goals of computer vision is the understanding of visual scenes. Scene understanding involves numerous tasks including recognizing what objects are present, localizing the objects in 2D and 3D, determining the objects’ and scene’s attributes, characterizing relationships between objects and providing a semantic description of the scene. The current object classification and detection datasets [@Imagenet; @PASCAL; @SUN; @Dollar2012PAMI] help us explore the first challenges related to scene understanding. For instance the ImageNet dataset [@Imagenet], which contains an unprecedented number of images, has recently enabled breakthroughs in both object classification and detection research [@Hinton; @GirshickDDM13; @OverFeat]. The community has also created datasets containing object attributes [@farhadi2009describing], scene attributes [@Patterson2012SunAttributes], keypoints [@bourdev2009poselets], and 3D scene information [@NYUDepth]. This leads us to the obvious question: what datasets will best continue our advance towards our ultimate goal of scene understanding?

![While previous object recognition datasets have focused on (a) image classification, (b) object bounding box localization or (c) semantic pixel-level segmentation, we focus 