• MineralImage5k: A benchmark for zero-shot raw mineral visual recognition and description 

  Nesteruk S.; Agafonova J.; Pavlov I.; Gerasimov M.; Latyshev N.; Dimitrov D.; Kuznetsov A.; Kadurin A.; Plechov P. (2023)

  Mineral image recognition is a challenging computer vision problem. Without external tools, even a human expert cannot distinguish some mineral species accurately. Previous research was mainly focused on processed mineral ...
• Deep Convolutions for In-Depth Automated Rock Typing 

  Baraboshkin E.E.; Ismailova L.S.; Orlov D.M.; Zhukovskaya E.A.; Kalmykov G.A.; Khotylev O.V.; Baraboshkin E.Yu.; Koroteev E.A. (2020)

  The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed ...
• GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning 

  Qinjun Qiu; Zhong Xie; Liang Wu; Liufeng Tao (2019)

  A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage ...
• Learning Foundation Language Models for Geoscience Knowledge Understanding and Utilization 

  Deng C.; Zhang T.; He Z.; Chen Q.; Shi Y.; Zhou L.; Fu L.; Zhang W.; Wang X.; Zhou C.; Lin Z.; He J. (2023)

  Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objective of advancing research and applications ...
• Geoscience language models and their intrinsic evaluation 

  Lawley C.J.M.; Raimondo S.; Chen T.; Brin L.; Zakharov A.; Kur D.; Hui J.; Newton G.; Burgoyne S.L.; Marquis G. (2022)

  Geoscientists use observations and descriptions of the rock record to study the origins and history of our planet, which has resulted in a vast volume of scientific literature. Recent progress in natural language processing ...

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