Pengpeng Jian1, Fucheng Guo1,*, Yanli Wang2, Yang Li1
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1707-1728, 2023, DOI:10.32604/cmes.2023.023243
- 06 February 2023
Abstract This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data. A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems. Existing methods either focus on single-modal or multimodal problems, and they cannot fit each other. A general geometry problem solver should obviously be able to process various modal problems at the same time. In this paper, a shared feature-learning model of multimodal data is adopted to learn the unified feature representation More >
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