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• W4207063631 abstract "People can accurately describe an image by constantly referring to the visual information and key text information of the image. Inspired by this idea, we propose the VTR-PTM (Visual-Text Reference Pretraining Model) for image captioning. First, based on the pretraining model (BERT/UNIML), we design the dual-stream input mode of image reference and text reference and use two different mask modes (bidirectional and sequence to sequence) to realize the VTR-PTM suitable for generating tasks. Second, the target dataset is used to fine tune the VTR-PTM. To the best of our knowledge, VTR-PTM is the first reported pretraining model to use visual-text references in the learning process. To evaluate the model, we conduct several experiments on the benchmark datasets of image captioning, including MS COCO and Visual Genome, and achieve significant improvements on most metrics. The code is available at https://github.com/lpfworld/VTR-PTM." @default.
• W4207063631 created "2022-01-26" @default.
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• W4207063631 date "2022-01-21" @default.
• W4207063631 modified "2023-09-26" @default.
• W4207063631 title "Visual-Text Reference Pretraining Model for Image Captioning" @default.
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• W4207063631 doi "https://doi.org/10.1155/2022/9400999" @default.
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