Update README.md

README.md

@@ -2,52 +2,50 @@
 
 ## 📖 Overview
 
-Magic Bench is a comprehensive evaluation dataset designed for text-to-image generation models. It contains 377 carefully curated prompts with detailed annotations across multiple dimensions, providing both Chinese and English versions for cross-lingual evaluation.
+Magic Bench is a comprehensive evaluation dataset designed for text-to-image generation models. It contains 377 carefully designed prompts with detailed labels across application scenarios and objective capabilities, providing both Chinese and English versions for cross-lingual evaluation.
 
 ## 🎯 Dataset Features
-
-- **377 evaluation prompts** covering diverse scenarios
-- **Bilingual support**: Both Chinese and English prompts
-- **Multi-dimensional annotations**: 9 different evaluation dimensions
-- **Comprehensive coverage**: Aesthetic design and artistic photography scenarios
-
+- **Systematic and Comprehensive Categorization**: We develop a taxonomy that systematically captures the core capabilities and application scenarios of T2I models.
+- **Multiple Test Points per Prompt**: To better reflect the user perspective, Magic-Bench-377 embeds multiple capabilities within a single prompt.
+- **Clarity and Visualizability**: Prompts should be concise, explicit and easy to visualize, while avoiding vague or non-visualizable descriptions
+- **Neutrality and Fairness.**: Descriptions involving regional specificity, references to celebrities, or copyrighted characters must be avoided to ensure fair evaluation across all models.
+- 
 ## 📊 Dataset Structure
 
 The dataset includes the following fields:
 
 | Field | Description |
 |-------|-------------|
-| `prompt_text_cn`| Chinese version of the prompt |
-| `prompt_text_en`| English version of the prompt |
-| `Application Scenario`| The application context |
-| `Expression Form`| Form of expression annotations |
-| `Element Composition`| Element combination patterns |
-| `Entity Description`| Entity description types |
+| `Prompt_text_cn`| Chinese version of the prompt |
+| `Prompt_text_en`| English version of the prompt |
+| `Application Scenario`| To account for the diversity of real world, magic bench 377 is divided  into five categories |
+| `Expression Form`| Refers to semantic units not directly pointing to visual elements but testing model’s understanding and reasoning over special forms of expressio |
+| `Element Composition`| Refers to visual elements or information arising from the combination of multiple element |
+| `Element`| Refers to visual elements or information that can be expressed by a single semantic unit, typically a word |
 
-## 🏷️ Annotation Dimensions
+## 🏷️ Taxonomy introduction
 
 ### 1. Application Scenario 
-- **Aesthetic design**: Logo design, character design, product design, etc.
-- **Art** : Photography, artistic creation, etc.
-- **Entertainment** : Entertainment and personalized content
-- **Film** : Film and storytelling scenarios
-- **Functional design** : Efficiency and functional design
+- **Aesthetic design**: Focuses on model use as a visual tool in professional design contexts, such as poster design, logo design, product design, etc. Models are expected to provide visually appealing outputs with high aesthetic quality.
+- **Art** : Focuses on user needs for high-level artistic creation, requiring models to generate outputs aligned with artistic styles, aesthetics, and visual imagination, such as oil painting, watercolor, sketching, or abstract expression.
+- **Entertainment** : Focuses on user needs for casual, creative and entertaining content, often reflecting internet culture (e.g., memes, emojis, or playful illustrations). The goal is to stimulate fun, amusement, or humor.
+- **Film** : Focuses on user needs for story-driven content creation, such as storyboards, cinematic scenes, or animated sequences. Models are expected to understand narrative details and generate scenes with coherent environments and character interactions.
+- **Functional design** : Focuses on user needs for practical work and learning materials, such as teaching slides, product manuals, or office diagrams. Outputs emphasize clarity, conciseness and informativeness.
 
 ### 2. Expression Form 
-- **Pronoun Reference**: Contains pronoun references
-- **Negation**: Contains negative expressions
-- **consistency**: Requires consistent elements
+- **Pronoun Reference**: Pronouns (he, she, it, they) referring back to entities mentioned earlier in the text, requiring the model to resolve co-reference.
+- **Negation**: Negative expressions such as "no", "without" or "does not".
+- **Consistency**: Multiple entities of the same type sharing the same attribute.
 
-### 3. Element Combination 
-- **Anti-Realism**: Anti-realistic combinations
-- **Multi-Entity Feature Matching**: Complex multi-entity combinations
-- **Layout & Typography**: Specific layout requirements
+### 3. Element Composition 
+- **Anti-Realism**: Combinations that contradict real-world cognition or physical laws.
+- **Multi-Entity Feature Matching**: Multiple entities of the same type with distinct attribute values.
+- **Layout & Typography**: Descriptions of spatial or positional relationships among images, text, or symbols.
 
-### 4. Entity Description 
-- **attribute** : Attribute descriptions
-- **relation** : Relationship descriptions
-- **Action/State** : Action or state descriptions
-- **quantity** : Quantity specifications
+### 4. Element 
+- **Entity**: Semantic units referring to entities such as people, animals, scenes, costumes, and decorations, including real-world and virtual entities, man-made objects, and natural elements.
+- **Entity Description**: Semantic units describing the quantity, attributes, forms, states, or relationships of entities.
+- **Image Description**: Semantic units that describe visual elements of a scene, including style, aesthetics, and artistic knowledge.
 
 ## 📁 Files
 
@@ -69,11 +67,6 @@ df_cn = pd.read_csv('magic_bench_chinese.csv')
 # Load English version
 df_en = pd.read_csv('magic_bench_english.csv')
 
-# Example: Filter prompts with style requirements
-stylized_prompts = df[df['是否有风格'] == '是']
-
-# Example: Get aesthetic design prompts
-aesthetic_prompts = df[df['应用场景'] == '创意设计']
 ```
 
 ## 📈 Statistics
@@ -88,9 +81,8 @@ aesthetic_prompts = df[df['应用场景'] == '创意设计']
 ## 🎯 Use Cases
 
 1. **Model Evaluation**: Comprehensive evaluation of text-to-image models
-2. **Benchmark Comparison**: Compare different models across various dimensions
-3. **Research**: Study model capabilities in different scenarios
-4. **Fine-tuning**: Use as training or validation data for model improvement
+2. **Research**: Study model capabilities in different scenarios
+3. **Fine-tuning**: Use as training or validation data for model improvement
 
 ## 📄 Citation