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- W4287550304 abstract "We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to contrast in a sub-word level, where from each image we extract several positive pairs and multiple negative examples. To yield effective visual representations for text recognition, we further suggest novel augmentation heuristics, different encoder architectures and custom projection heads. Experiments on handwritten text and on scene text show that when a text decoder is trained on the learned representations, our method outperforms non-sequential contrastive methods. In addition, when the amount of supervision is reduced, SeqCLR significantly improves performance compared with supervised training, and when fine-tuned with 100% of the labels, our method achieves state-of-the-art results on standard handwritten text recognition benchmarks." @default.
- W4287550304 created "2022-07-25" @default.
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- W4287550304 date "2020-12-20" @default.
- W4287550304 modified "2023-10-14" @default.
- W4287550304 title "Sequence-to-Sequence Contrastive Learning for Text Recognition" @default.
- W4287550304 doi "https://doi.org/10.48550/arxiv.2012.10873" @default.
- W4287550304 hasPublicationYear "2020" @default.
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