ML Anthology

Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection

Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, Sanghyun Park
AAAI 2024 pp. 8591-8599
doi:10.1609/AAAI.V38I8.28703 /aaai/2024/kim2024aaai-few/
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Cite

Text

Kim et al. "Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I8.28703

Markdown

[Kim et al. "Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/kim2024aaai-few/) doi:10.1609/AAAI.V38I8.28703

BibTeX

@inproceedings{kim2024aaai-few,
  title     = {{Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection}},
  author    = {Kim, Soopil and An, Sion and Chikontwe, Philip and Kang, Myeongkyun and Adeli, Ehsan and Pohl, Kilian M. and Park, Sanghyun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {8591-8599},
  doi       = {10.1609/AAAI.V38I8.28703},
  url       = {https://mlanthology.org/aaai/2024/kim2024aaai-few/}
}