Multi-modal pre-training model acquisition method, electronic device and storage medium
AssigneeBeijing Baidu Netcom Science Technology Co., Ltd.
InventorsFei YU, Jiji TANG, Weichong YIN, Yu SUN, Hao TIAN, Hua WU, Haifeng WANG
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Abstract
A multi-modal pre-training model acquisition method, an electronic device and a storage medium, which relate to the fields of deep learning and natural language processing, are disclosed. The method may include: determining, for each image-text pair as training data, to-be-processed fine-grained semantic word in the text; masking the to-be-processed fine-grained semantic words; and training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.
Claims20
A multi-modal pre-training model acquisition method, comprising:
- determining, for each image-text pair as training data, to-be-processed fine-grained semantic words in the text;
- masking the to-be-processed fine-grained semantic words;
- and
- training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.
An electronic device, comprising:
- at least one processor;
- and
- a memory in communication connection with the at least one processor;
- wherein
- the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to carry out a multi-modal pre-training model acquisition method, which comprises:
- determining, for each image-text pair as training data, to-be-processed fine-grained semantic words in the text;
- masking the to-be-processed fine-grained semantic words;
- and
- training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.
A non-transitory computer-readable storage medium comprising instructions, which, when executed by a computer, cause the computer to carry out a multi-modal pre-training model acquisition method, which comprises:
- determining, for each image-text pair as training data, to-be-processed fine-grained semantic words in the text;
- masking the to-be-processed fine-grained semantic words;
- and
- training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.
Dependencies are inferred from the claim text.