FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

@article{Kim2017FCSSFC,
  title={FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence},
  author={Seungryong Kim and Dongbo Min and Bumsub Ham and Stephen Lin and Kwanghoon Sohn},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2017},
  volume={41},
  pages={581-595}
}
We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. Unlike traditional dense correspondence approaches for estimating depth or optical flow, semantic correspondence estimation poses additional challenges due to intra-class appearance and shape variations among different instances within the same object or scene category. To robustly match points across semantically similar images, we formulate FCSS using local self-similarity (LSS… CONTINUE READING

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