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      Places: A 10 Million Image Database for Scene Recognition

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          Abstract

          The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems.

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          Most cited references13

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          Microsoft COCO: Common Objects in Context

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            CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

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              Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

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                Author and article information

                Journal
                IEEE Transactions on Pattern Analysis and Machine Intelligence
                IEEE Trans. Pattern Anal. Mach. Intell.
                Institute of Electrical and Electronics Engineers (IEEE)
                0162-8828
                2160-9292
                June 1 2018
                June 1 2018
                : 40
                : 6
                : 1452-1464
                Article
                10.1109/TPAMI.2017.2723009
                28692961
                dff3c7e4-5fff-4fd3-9cdd-5750310ae4cc
                © 2018

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