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• W4361762480 abstract "Cross-domain text recognition is a very challenging task due to the domain drift problem. One solution is aligning feature distributions between domains through Unsupervised Domain Adaptation (UDA). All existing methods perform feature alignment based on the whole image or semantic character features. However, visual character features without contextual semantics also contain much valuable information, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>e.g</i> ., stroke features of individual characters, which also benefits domain transfer. To this end, we propose a dual intra-Class Aggregation based unsupervised Domain Adaptation method (CADA) for text recognition, which aligns both visual and semantic character feature distributions. To our knowledge, CADA is the first to consider visual character features without contextual semantics in cross-domain text recognition tasks. Accordingly, a Single-head Self-Attention (SSA) mechanism is introduced for extracting visual character features. Thereafter, a dual intra-class aggregation strategy is designed, which performs class aggregations in both visual and semantic spaces. We test the proposed method on widely-used datasets by combining it with representative text recognition models with various decoding methods. Extensive experimental results demonstrate the superiority and generality. Moreover, there is no additional inference time introduced compared to the baselines." @default.
• W4361762480 created "2023-04-04" @default.
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• W4361762480 date "2023-10-01" @default.
• W4361762480 modified "2023-10-15" @default.
• W4361762480 title "Unsupervised Domain Adaptation via Class Aggregation for Text Recognition" @default.
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• W4361762480 doi "https://doi.org/10.1109/tcsvt.2023.3262692" @default.
• W4361762480 hasPublicationYear "2023" @default.
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