Create README.md

README.md

@@ -0,0 +1,29 @@
+---
+license: mit
+---
+# ConText
+
+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.
+
+The code source is in [here](https://github.com/Ferenas/ConText).
+
+# Model Weight & Usage
+
+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! 
+
+## Model Performance
+
+It reaches SOTA performance in all text segmentation and removal benchmarks.
+
+## Model Card Contact
+
+Feel free to contact [email protected] if you have any problem!
+[More Information Needed]