COCO数据集:本数据集包含了91种物体类型的图像,这些物体类型能够被4岁大小的孩子豪不费力的识别出来。数据集有32.8万张图片,包含有250万个标注实例。 标注工具:Microsoft自研 类别确定 使用多个数据源来建立顶层物体类别: 1、首先联系PASCAL VOC数据集的类别, 2、1200种最经常见到的视觉可分辨的物体的一个子集(来自...
Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, Larry Zitnick ECCV|September 2014 Published by European Conference on Computer Vision Publication
论文学习:Microsoft COCO: Common Objects in Context Abstract 为了促进物体识别的发展,COCO数据集是在把物体识别放到更广泛的场景理解问题下的情况下被制作而成的。 每个物体都被单独分割标注,这样做的目的是进行精确的物体定位。 数据集包含91种物体类别,2500000个被标注的物体,328000张图片 INTRODUCTION 论文要点 对...
标注类型:实例分割 该数据集的主要创新:实例是放在natural context中的,并且语境有很多种而非人为定义的背景中,看Fig1就懂这一点创新了 本文工作 制作该数据集 统计分析该数据集,并与其它3个数据集做比较 使用DPM,提供该数据集的目标检测和实例分割的baseline,并分析其performance 引言 场景理解包含的任务 图像分类:...
Microsoft COCO: Common Objects in Context Tsung-Yi Lin Michael Maire Serge Belongie Lubomir Bourdev Ross Girshick James Hays Pietro Perona Deva Ramanan C. Lawrence Zitnick Piotr Doll ´ ar Abstract—We present a new dataset with the goal of advancing the state-of-the-art in object recognition...
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects i...
Microsoft COCO: Common Objects in Context Tsung-Yi Lin 1 , Michael Maire 2 , Serge Belongie 1 , James Hays 3 , Pietro Perona 2 , Deva Ramanan 4 , Piotr Doll´ar 5 , C. Lawrence Zitnick 5 1 Cornell, 2 Caltech, 3 Brown, 4 UC Irvine, 5 Microsoft Research Abstract. We present ...
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex ev
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex ev
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