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- W4375959209 abstract "Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of textbf{93.72}, textbf{54.39}, textbf{84.65} and textbf{98.04} respectively under one billion parameters. The code is made publicly available at: href{https://github.com/ayanban011/SwinDocSegmenter}{github.com/ayanban011/SwinDocSegmenter}" @default.
- W4375959209 created "2023-05-10" @default.
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- W4375959209 date "2023-05-08" @default.
- W4375959209 modified "2023-10-14" @default.
- W4375959209 title "SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation" @default.
- W4375959209 doi "https://doi.org/10.1007/978-3-031-41676-7_18" @default.
- W4375959209 hasPublicationYear "2023" @default.
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