ConText: Driving In-context Learning for Text Removal and Segmentation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912412971565056 |
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| author | Zhang, Fei Zhang, Pei Yang, Baosong Huang, Fei Wang, Yanfeng Zhang, Ya |
| author_facet | Zhang, Fei Zhang, Pei Yang, Baosong Huang, Fei Wang, Yanfeng Zhang, Ya |
| contents | This paper presents the first study on adapting the visual in-context learning (V-ICL) paradigm to optical character recognition tasks, specifically focusing on text removal and segmentation. Most existing V-ICL generalists employ a reasoning-as-reconstruction approach: they turn to using a straightforward image-label compositor as the prompt and query input, and then masking the query label to generate the desired output. This direct prompt confines the model to a challenging single-step reasoning process. To address this, we propose a task-chaining compositor in the form of image-removal-segmentation, providing an enhanced prompt that elicits reasoning with enriched intermediates. Additionally, we introduce context-aware aggregation, integrating the chained prompt pattern into the latent query representation, thereby strengthening the model's in-context reasoning. We also consider the issue of visual heterogeneity, which complicates the selection of homogeneous demonstrations in text recognition. Accordingly, this is effectively addressed through a simple self-prompting strategy, preventing the model's in-context learnability from devolving into specialist-like, context-free inference. Collectively, these insights culminate in our ConText model, which achieves new state-of-the-art across both in- and out-of-domain benchmarks. The code is available at https://github.com/Ferenas/ConText. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ConText: Driving In-context Learning for Text Removal and Segmentation Zhang, Fei Zhang, Pei Yang, Baosong Huang, Fei Wang, Yanfeng Zhang, Ya Computer Vision and Pattern Recognition This paper presents the first study on adapting the visual in-context learning (V-ICL) paradigm to optical character recognition tasks, specifically focusing on text removal and segmentation. Most existing V-ICL generalists employ a reasoning-as-reconstruction approach: they turn to using a straightforward image-label compositor as the prompt and query input, and then masking the query label to generate the desired output. This direct prompt confines the model to a challenging single-step reasoning process. To address this, we propose a task-chaining compositor in the form of image-removal-segmentation, providing an enhanced prompt that elicits reasoning with enriched intermediates. Additionally, we introduce context-aware aggregation, integrating the chained prompt pattern into the latent query representation, thereby strengthening the model's in-context reasoning. We also consider the issue of visual heterogeneity, which complicates the selection of homogeneous demonstrations in text recognition. Accordingly, this is effectively addressed through a simple self-prompting strategy, preventing the model's in-context learnability from devolving into specialist-like, context-free inference. Collectively, these insights culminate in our ConText model, which achieves new state-of-the-art across both in- and out-of-domain benchmarks. The code is available at https://github.com/Ferenas/ConText. |
| title | ConText: Driving In-context Learning for Text Removal and Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.03799 |