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@@ -51,15 +51,47 @@ An example with the original VQA and our proposed coarse-to-fine CoT process is
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  ![A data example with the original VQA compared with our coarse-to-fine CoT VQA.](./example.png)
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  ### STORM Prompts
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- ***Generating the dataset for IQA***
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  ```
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  <image> You are now an advanced Image Quality Evaluator, and your task is to assess the quality of the provided image. Please evaluate the image’s quality based on a 5-rate scale: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent). Please provide the coarse category that can help you answer the question better. Please first coarsely categorise the image: rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair). Based on the coarse classification, proceed to make a final rate prediction. The specific steps are as follows:
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  1. Make the coarse prediction with the candidates:rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair).
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  2. Based on the coarse classification, proceed to make a final age prediction with the candidates: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent).
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  3. Please note that the coarse thoughts and the final answer should be consistent.
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  Answer: [Coarse answer], [Final answer]
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  ```
 
 
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  ## Evaluation
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  ## Examples
 
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  ![A data example with the original VQA compared with our coarse-to-fine CoT VQA.](./example.png)
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  ### STORM Prompts
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+ *Generating the dataset for IQA*
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  ```
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  <image> You are now an advanced Image Quality Evaluator, and your task is to assess the quality of the provided image. Please evaluate the image’s quality based on a 5-rate scale: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent). Please provide the coarse category that can help you answer the question better. Please first coarsely categorise the image: rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair). Based on the coarse classification, proceed to make a final rate prediction. The specific steps are as follows:
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  1. Make the coarse prediction with the candidates:rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair).
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  2. Based on the coarse classification, proceed to make a final age prediction with the candidates: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent).
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  3. Please note that the coarse thoughts and the final answer should be consistent.
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+ Answer: [Coarse answer], [Final answer]
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+
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+ ```
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+ *Generating the dataset for IAA*
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+ ```
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+ <image> You are now an advanced Aesthetic Evaluation Evaluator, and your task is to assess the aesthetic quality of the provided image. Please evaluate the image’s aesthetic quality based on a 5-level scale: level0(Unacceptable), level1(Flawed), level2(Average), level3(Professional), level4(Excellent). Please first coarsely categorise the image: level0-1(Below Average), level2(Average), level3-4(Above Average). Based on the coarse classification, proceed to make a final level prediction. The specific steps are as follows:
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+ 1. Make the coarse prediction with the candidates:level0-1(Below Average), level2(Average), level3-4(Above Average).
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+ 2. Based on the coarse classification, proceed to make a final age prediction with the candidates: level0(Unacceptable), level1(Flawed), level2(Average), level3(Professional), level4(Excellent).
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+ 3. Please note that the coarse thoughts and the final answer should be consistent.
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+
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+ Answer: [Coarse answer], [Final answer]
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+ ```
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+
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+ *Generating the dataset for MDG*
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+ ```
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+ <image> You are an experienced facial analysis expert, and you need to estimate the age group of the person in the provided facial image based on their facial features. The known age range of the image is from 16 to 77 years old. Please first coarsely categorise the image: Teenager(16-24 years old), Adult(25-47 years old), Elder(48+ years old). Based on the coarse classification, proceed to make a final age prediction.The final output should be in the format: Coarse Answer: [result], Predicted Age: [result]. The specific steps are as follows:
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+ 1. Make the coarse prediction with the candidates: Teenager(16-24 years old), Adult(25-47 years old), Elder(48+ years old).
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+ 2. Based on the coarse classification, proceed to make a final age prediction with the candidates: from 16 to 77 years old.
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+ 3. Please note that the coarse thoughts and the final answer should be consistent.
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+
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+ Answer: [Coarse answer], [Final answer]
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+ ```
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+
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+ *Generating the dataset for HDE*
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+ ```
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+ <image> You are now an advanced history researcher, and you need to grade the provided images by decade. These are all candidate categories: phase0(1930s), phase1(1940s), phase2(1950s), phase3(1960s), and phase4(1970s). Please first coarsely categorise the image: Early(phase0-phase1), Mid(phase2), Late(phase3-phase4). Based on the coarse classification, proceed to make a final phase prediction.The final output should be in the format: Coarse Classification: [result], Predicted Phase: [result]. The specific steps are as follows:
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+ 1. Make the coarse prediction with the candidates: Early(phase0-phase1), Mid(phase2), Late(phase3-phase4).
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+ 2. Based on the coarse classification, proceed to make a final age prediction with the candidates: phase0(1930s), phase1(1940s), phase2(1950s), phase3(1960s), and phase4(1970s).
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+ 3. Please note that the coarse thoughts and the final answer should be consistent.
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+
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  Answer: [Coarse answer], [Final answer]
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  ```
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+
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+
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  ## Evaluation
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  ## Examples