ConText: Driving In-context Learning for Text Removal and Segmentation

• URL: http://arxiv.org/abs/2506.03799v1
• Date: Wed, 04 Jun 2025 10:06:32 GMT
• Title: ConText: Driving In-context Learning for Text Removal and Segmentation
• Authors: Fei Zhang, Pei Zhang, Baosong Yang, Fei Huang, Yanfeng Wang, Ya Zhang,
• Abstract summary: This paper presents the first study on adapting the visual in-context learning paradigm to optical character recognition tasks.<n>We propose a task-chaining compositor in the form of image-removal-segmentation.<n>We also introduce context-aware aggregation, integrating the chained prompt pattern into the latent query representation.
• Score: 59.6299939669307
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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