Learning an optimisable semantic segmentation map with image conditioned variational autoencoder

Pengcheng Zhuang, Yusuke Sekikawa, Kosuke Hara, Hideo Saito

研究成果: Conference contribution

抄録

Recent semantic segmentation systems have achieved significant improvement by performing pixel-wise training with hierarchical features using deep convolutional neural network models. While the learning process usually requires pixel-level annotated images, it is difficult to get desirable amounts of fine-labeled data and thus the training set size is more likely to be limited, often in thousands. This means that top methods for a dataset can be fine-tuned for a specific situation, making the generalization ability unclear. In real-world applications like self-driving systems, ambiguous region or lack of context information can cause errors in the predicted results. Resolving such ambiguities is crucial for subsequent operations to be performed safely. We are inspired by work from CodeSLAM where optimizable pixel-wise depth representation is learned. We modify the regression method to work on the pixel-wise classification problem. By training a variational auto-encoder network conditioned with a color image, the computed latent space works as a low-dimensional representation of semantic segmentation, which can be efficiently optimized. As a consequence, our model can correct the error or ambiguity of the prediction during the inference phase given useful scene information. We show how this approach works by giving partial scene truth and perform optimization on the latent variable.

本文言語English
ホスト出版物のタイトルImage Analysis and Processing – ICIAP 2019 - 20th International Conference, Proceedings
編集者Elisa Ricci, Nicu Sebe, Samuel Rota Bulò, Cees Snoek, Oswald Lanz, Stefano Messelodi
出版社Springer Verlag
ページ379-389
ページ数11
ISBN(印刷版)9783030306441
DOI
出版ステータスPublished - 2019
イベント20th International Conference on Image Analysis and Processing, ICIAP 2019 - Trento, Italy
継続期間: 2019 9月 92019 9月 13

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11752 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference20th International Conference on Image Analysis and Processing, ICIAP 2019
国/地域Italy
CityTrento
Period19/9/919/9/13

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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