{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","arxiv_id":"1811.12814","date":"2018-11-30","proceeding":"CVPR 2019 6","authors":["Yunpeng Chen","Marcus Rohrbach","Zhicheng Yan","Shuicheng Yan","Jiashi Feng","Yannis Kalantidis"],"abstract":"Globally modeling and reasoning over relations between regions can be\nbeneficial for many computer vision tasks on both images and videos.\nConvolutional Neural Networks (CNNs) excel at modeling local relations by\nconvolution operations, but they are typically inefficient at capturing global\nrelations between distant regions and require stacking multiple convolution\nlayers. In this work, we propose a new approach for reasoning globally in which\na set of features are globally aggregated over the coordinate space and then\nprojected to an interaction space where relational reasoning can be efficiently\ncomputed. After reasoning, relation-aware features are distributed back to the\noriginal coordinate space for down-stream tasks. We further present a highly\nefficient instantiation of the proposed approach and introduce the Global\nReasoning unit (GloRe unit) that implements the coordinate-interaction space\nmapping by weighted global pooling and weighted broadcasting, and the relation\nreasoning via graph convolution on a small graph in interaction space. The\nproposed GloRe unit is lightweight, end-to-end trainable and can be easily\nplugged into existing CNNs for a wide range of tasks. Extensive experiments\nshow our GloRe unit can consistently boost the performance of state-of-the-art\nbackbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D\nand 3D CNNs, on image classification, semantic segmentation and video action\nrecognition task.","url_abs":"http://arxiv.org/abs/1811.12814v1","url_pdf":"http://arxiv.org/pdf/1811.12814v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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