{"type":"ScholarlyArticle","title":"Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition","authors":[{"name":"Zuan Gao","slug":"zuan-gao","given":"Zuan","family":"Gao","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Yuxin Wang","slug":"yuxin-wang","given":"Yuxin","family":"Wang","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Yadong Qu","slug":"yadong-qu","given":"Yadong","family":"Qu","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Boqiang Zhang","slug":"boqiang-zhang","given":"Boqiang","family":"Zhang","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Zixiao Wang","slug":"zixiao-wang","given":"Zixiao","family":"Wang","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Jianjun Xu","slug":"jianjun-xu","given":"Jianjun","family":"Xu","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Hongtao Xie","slug":"hongtao-xie","given":"Hongtao","family":"Xie","affiliation":"University of Science and Technology of China , Hefei , China"}],"abstract":"In text recognition, self-supervised pre-training emerges as a good solution to reduce dependence on expansive annotated real data. Previous studies primarily focus on local visual representation by leveraging mask image modeling or sequence contrastive learning. However, they omit modeling the linguistic information in text images, which is crucial for recognizing text. To simultaneously capture local character features and linguistic information in visual space, we propose Symmetric Superimposition Modeling (SSM). The objective of SSM is to reconstruct the direction-specific pixel and feature signals from the symmetrically superimposed input. Specifically, we add the original image with its inverted views to create the symmetrically superimposed inputs. At the pixel level, we reconstruct the original and inverted images to capture character shapes and texture-level linguistic context. At the feature level, we reconstruct the feature of the same original image and inverted image with different augmentations to model the semantic-level linguistic context and the local character discrimination. In our design, we disrupt the character shape and linguistic rules. Consequently, the dual-level reconstruction facilitates understanding character shapes and linguistic information from the perspective of visual texture and feature semantics. Experiments on various text recognition benchmarks demonstrate the effectiveness and generality of SSM, with 4.1\\% average performance gains and 86.6% new state-of-the-art average word accuracy on Union14M benchmarks. The code is available at https://github.com/FaltingsA/SSM.","doi":"10.24963/ijcai.2024/85","url":"https://ijcai.org/proceedings/2024/85","pdf":"https://ijcai.org/proceedings/2024/0085.pdf","markdown":"https://ijcai.org/proceedings/2024/85.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/85","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":85,"pages":{"first":767,"last":775},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Representation learning","Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"[Aberdam et al., 2021] Aviad Aberdam, Ron Litman, Sha- har Tsiper, Oron Anschel, Ron Slossberg, Shai Mazor, R. 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