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Weakly Supervised Learning of Dense Semantic Correspondences and Segmentation

  • Nikolai UferEmail author
  • Kam To Lui
  • Katja Schwarz
  • Paul Warkentin
  • Björn Ommer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)

Abstract

Finding semantic correspondences is a challenging problem. With the breakthrough of CNNs stronger features are available for tasks like classification but not specifically for the requirements of semantic matching. In the following we present a weakly supervised learning approach which generates stronger features by encoding far more context than previous methods. First, we generate more suitable training data using a geometrically informed correspondence mining method which is less prone to spurious matches and requires only image category labels as supervision. Second, we introduce a new convolutional layer which is a learned mixture of differently strided convolutions and allows the network to encode much more context while preserving matching accuracy at the same time. The strong geometric encoding on the feature side enables us to learn a semantic flow network, which generates more natural deformations than parametric transformation based models and is able to predict foreground regions at the same time. Our semantic flow network outperforms current state-of-the-art on several semantic matching benchmarks and the learned features show astonishing performance regarding simple nearest neighbor matching.

Notes

Acknowledgment

This work has been supported in part by the DFG grand OM81/1-1 and a hardware donation from NVIDIA Corporation.

Supplementary material

480714_1_En_32_MOESM1_ESM.pdf (4.1 mb)
Supplementary material 1 (pdf 4223 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nikolai Ufer
    • 1
    Email author
  • Kam To Lui
    • 1
  • Katja Schwarz
    • 1
  • Paul Warkentin
    • 1
  • Björn Ommer
    • 1
  1. 1.Heidelberg University, HCI/IWRHeidelbergGermany

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