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| license: mit |
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| # ConText |
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| This is **[ConText](https://arxiv.org/abs/2506.03799)**, a powerful generalists that could do perfect text removal and segmentation framework. |
| We also first exploration of establishing a visual in-context learning (V-ICL) paradigm for fine-grained text recognition tasks, including text segmentation and removal. To achieve this, we sought a single-task-targeted baseline solution based on the prevailing V-ICL frameworks, which typically regulates in-context inference as a query-label-reconstruction process. Beyond simple task-specific fine-tuning, we proposed an end-to-end in-context generalist elicited from a task-chaining prompt that explicitly chaining up tasks as one enriched demonstration, leveraging inter-task correlations to improve the in-context reasoning capabilities. A Through quantitative and |
| qualitative experiments, we demonstrated the grounding |
| effectiveness and superiority of our framework across various |
| in-domain and out-of-domain text recognition tasks, |
| outperforming both current generalists and specialists. Overall, |
| we hope this pioneering work will encourage further |
| development of V-ICL in text recognition. |
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| The code source is in [here](https://github.com/Ferenas/ConText). |
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| # Model Weight & Usage |
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| Here we provide the weights of ConText and ConTextV, you can download these checkpoints and follow the process in [here](https://github.com/Ferenas/ConText) |
| to perform OCR-level removal and segmentation. Have FUN! |
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| ## Model Performance |
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| It reaches SOTA performance in all text segmentation and removal benchmarks. |
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| ## Model Card Contact |
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| Feel free to contact ferenas@sjtu.edu.cn if you have any problem! |
| [More Information Needed] |
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