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Object-Contextual Representations for Semantic Segmentation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12351)

Abstract

In this paper, we study the context aggregation problem in semantic segmentation. Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we present a simple yet effective approach, object-contextual representations, characterizing a pixel by exploiting the representation of the corresponding object class. First, we learn object regions under the supervision of the ground-truth segmentation. Second, we compute the object region representation by aggregating the representations of the pixels lying in the object region. Last, we compute the relation between each pixel and each object region, and augment the representation of each pixel with the object-contextual representation which is a weighted aggregation of all the object region representations. We empirically demonstrate our method achieves competitive performance on various benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff. Our submission “HRNet + OCR + SegFix” achieves the \({1}^{\mathrm {st}}\) place on the Cityscapes leaderboard by the ECCV 2020 submission deadline. Code is available at: https://git.io/openseg and https://git.io/HRNet.OCR.

Keywords

Semantic segmentation Context aggregation 

Notes

Acknowledgement

This work is partially supported by Natural Science Foundation of China under contract No. 61390511, and Frontier Science Key Research Project CAS No. QYZDJ-SSW-JSC009.

Supplementary material

504443_1_En_11_MOESM1_ESM.pdf (1.2 mb)
Supplementary material 1 (pdf 1231 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Microsoft Research AsiaBeijingChina

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