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Guosheng Lin
Researcher at Nanyang Technological University
Publications - 193
Citations - 12611
Guosheng Lin is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 36, co-authored 153 publications receiving 8618 citations. Previous affiliations of Guosheng Lin include Salesforce.com & Association for Computing Machinery.
Papers
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Proceedings ArticleDOI
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
TL;DR: RefineNet is presented, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections and introduces chained residual pooling, which captures rich background context in an efficient manner.
Journal ArticleDOI
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
TL;DR: A deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF is presented, and a deep structured learning scheme which learns the unary and pairwise potentials of continuousCRF in a unified deep CNN framework is proposed.
Posted Content
Efficient piecewise training of deep structured models for semantic segmentation
TL;DR: This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
Posted Content
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
TL;DR: A deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework and can be used for depth estimations of general scenes with no geometric priors nor any extra information injected.
Proceedings ArticleDOI
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
TL;DR: Zhang et al. as discussed by the authors proposed a patch-patch context between image regions and patch-background context, and formulated conditional random fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.