ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1509.00552
73
151
v1v2 (latest)

DAG-Recurrent Neural Networks For Scene Labeling

2 September 2015
Bing Shuai
Zhen Zuo
Bernie Wang
G. Wang
ArXiv (abs)PDFHTML
Abstract

In image labeling, local representations for image units (pixels, patches or superpixels) are usually generated from their surrounding image patches, thus long-range contextual information is not effectively encoded. In this paper, we introduce recurrent neural networks (RNNs) to address this issue. Furthermore, directed acyclic graph RNNs (DAG-RNNs) are proposed to process DAG-structured data, which enables the network to model long-range semantic dependencies among image units. Our DAG-RNNs are capable of tremendously enhancing the discriminative power of local representations, which significantly benefits local classification. We achieve state-of-the-art results on the challenging CamVid, SiftFlow and Barcelona benchmarks.

View on arXiv
Comments on this paper