Update README.md

README.md

@@ -28,7 +28,7 @@ Please check out our [checkpoint_STORM](https://huggingface.co/datasets/ttlyy/OR
 ### Pretraining Dataset
 To ensure a robust foundation for different visual rating tasks, our STORM data collection deliberately integrates a diverse selection of data including image quality assessment (IQA), image aesthetic assessment (IAA), facial age estimation (FAE), medical disease grading (MDG), and image historical date estimation (HDE). These data domains are intentionally chosen to cultivate a comprehensive skill set across varied visual rating tasks.
 | Domain | Source Dataset | Full Version Size | Catergory |
-| --- |  --- |  --- | ---: |
+| --- |  --- |  --- | --- |
 | Image Quality Assessment (IQA) | SPAQ | 11,125 | 5 levels |
 | Image Quality Assessment (IQA) | ChallengeDB | 1,169 | 5 levels |
 | Image Quality Assessment (IQA) | KonIQ | 10,073 | 5 levels |
@@ -43,6 +43,10 @@ To ensure a robust foundation for different visual rating tasks, our STORM data
 | Medical Disease Grading (MDG) | DeepDR | 2,000 | 5 grades |
 | Medical Disease Grading (MDG) | APTOS | 3,662 | 5 grades |
 | Historical Date Estimation (HDE) | HCI | 1,325 | 5 decades |
+
+As these datasets provide only images and digital labels, they are designed with a standardized VQA paradigm by reusing their images and modifying the annotations into a textual form to enable MLLMs to undergo joint training for heterogeneous tasks of diverse domains. Specifically, each data sample originally consists of a simple question and a corresponding numeric answer. However, this paradigm can lead to numerical hallucination. Hence, we add extra domain-driven prompts and coarse-to-fine CoT to mitigate this issue.
+An example with the original VQA and our proposed coarse-to-fine CoT process is shown in the following figure. Meanwhile, we adopt the form of text + numbers for the labels to enhance semantic
+![CoT Process Visualization]()
 ## Evaluation
 
 ## Examples