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- W3034799230 abstract "Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup. However,for tree-structured representational markup, stringrepresentations can hardly cope with the structural complexity. In this work, we first showvia a set of toy problems that string decodersstruggle to decode tree structures, especially asstructural complexity increases, we then proposea tree-structured decoder that specifically aimsat generating a tree-structured markup. Our decoders works sequentially, where at each step achild node and its parent node are simultaneouslygenerated to form a sub-tree. This sub-tree is consequently used to construct the final tree structurein a recurrent manner. Key to the success of ourtree decoder is twofold, (i) it strictly respects theparent-child relationship of trees, and (ii) it explicitly outputs trees as oppose to a linear string.Evaluated on both math formula recognition andchemical formula recognition, the proposed treedecoder is shown to greatly outperform strongstring decoder baselines." @default.
- W3034799230 created "2020-06-19" @default.
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- W3034799230 date "2020-07-12" @default.
- W3034799230 modified "2023-10-02" @default.
- W3034799230 title "A Tree-Structured Decoder for Image-to-Markup Generation" @default.
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