Update README.md

README.md

@@ -9,7 +9,7 @@ language:
 size_categories:
 - 100B<n<1T
 ---
-* **`2024.08.20`** 🌟 We are proud to open-source MME-Unify, a comprehensive evaluation framework designed to systematically assess U-MLLMs. Our Benchmark covering 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.
+* **`2024.08.20`** 🌟 We are proud to open-source MME-Unify, a comprehensive evaluation framework designed to systematically assess U-MLLMs. Our Benchmark covers 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.
 
 
 Paper: arxiv.org/abs/2408.13257
@@ -20,69 +20,44 @@ Project page: https://aba122.github.io/MME-Unify.github.io/
 
 
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/ZnczJh10NHm0u03p7kjm_.png)
+![](leaderboard.png)
 
 
 ## How to use?
 
-Since the image files are large and have been split into multiple compressed parts, please first merge the compressed files with the same name and then extract them together.
+You can download images in this repository and the final structure should look like this:
 
 ```
-#!/bin/bash
-
-# Function to process each set of split files
-process_files() {
-    local part="$1"
-    
-    # Extract the base name of the file
-    local base_name=$(basename "$part" .tar.gz.part_aa)
-    
-    # Merge the split files into a single archive
-    cat "${base_name}".tar.gz.part_* > "${base_name}.tar.gz"
-    
-    # Extract the merged archive
-    tar -xzf "${base_name}.tar.gz"
-    
-    # Remove the individual split files
-    rm -rf "${base_name}".tar.gz.part_*
-
-    rm -rf "${base_name}.tar.gz"
-}
-
-export -f process_files
-
-# Find all .tar.gz.part_aa files and process them in parallel
-find . -name '*.tar.gz.part_aa' | parallel process_files
-
-# Wait for all background jobs to finish
-wait
-
-# nohup  bash unzip_file.sh >> unfold.log 2>&1 &
-
-
+MME-Unify
+├── CommonSense_Questions
+├── Conditional_Image_to_Video_Generation
+├── Fine-Grained_Image_Reconstruction
+├── Math_Reasoning
+├── Multiple_Images_and_Text_Interlaced
+├── Single_Image_Perception_and_Understanding
+├── Spot_Diff
+├── Text-Image_Editing
+├── Text-Image_Generation
+├── Text-to-Video_Generation
+├── Video_Perception_and_Understanding
+└── Visual_CoT
 ```
 
-# MME-RealWorld Data Card
-
 ## Dataset details
 
 
-Existing Multimodal Large Language Model benchmarks present several common barriers that make it difficult to measure the significant challenges that models face in the real world, including: 
-1) small data scale leads to a large performance variance; 
-2) reliance on model-based annotations results in restricted data quality; 
-3) insufficient task difficulty, especially caused by the limited image resolution.
+We present MME-Unify, a comprehensive evaluation framework designed to assess U-MLLMs systematically. Our benchmark includes:
 
-We present MME-RealWord, a benchmark meticulously designed to address real-world applications with practical relevance. Featuring 13,366 high-resolution images averaging 2,000 × 1,500 pixels, MME-RealWord poses substantial recognition challenges. Our dataset encompasses 29,429 annotations across 43 tasks, all expertly curated by a team of 25 crowdsource workers and 7 MLLM experts. The main advantages of MME-RealWorld compared to existing MLLM benchmarks as follows:
+1. **Standardized Traditional Task Evaluation** We sample from 12 datasets, covering 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.
 
-1. **Data Scale**: with the efforts of a total of 32 volunteers, we have manually annotated 29,429 QA pairs focused on real-world scenarios, making this the largest fully human-annotated benchmark known to date.
+2. **Unified Task Assessment** We introduce five novel tasks testing multimodal reasoning, including image editing, commonsense QA with image generation, and geometric reasoning.
 
-2. **Data Quality**: 1) Resolution: Many image details, such as a scoreboard in a sports event, carry critical information. These details can only be properly interpreted with high- resolution images, which are essential for providing meaningful assistance to humans. To the best of our knowledge, MME-RealWorld features the highest average image resolution among existing competitors. 2) Annotation: All annotations are manually completed, with a professional team cross-checking the results to ensure data quality.
+3. **Comprehensive Model Benchmarking** We evaluate 12 leading U-MLLMs, such as Janus-Pro, EMU3, and VILA-U, alongside specialized understanding (e.g., Claude-3.5) and generation models (e.g., DALL-E-3).
 
-3. **Task Difficulty and Real-World Utility.**: We can see that even the most advanced models have not surpassed 60% accuracy. Additionally, many real-world tasks are significantly more difficult than those in traditional benchmarks. For example, in video monitoring, a model needs to count the presence of 133 vehicles, or in remote sensing, it must identify and count small objects on a map with an average resolution exceeding 5000×5000.
+Our findings reveal substantial performance gaps in existing U-MLLMs, highlighting the need for more robust models capable of handling mixed-modality tasks effectively.
 
-4. **MME-RealWord-CN.**: Existing Chinese benchmark is usually translated from its English version. This has two limitations: 1) Question-image mismatch. The image may relate to an English scenario, which is not intuitively connected to a Chinese question. 2) Translation mismatch [58]. The machine translation is not always precise and perfect enough. We collect additional images that focus on Chinese scenarios, asking Chinese volunteers for annotation. This results in 5,917 QA pairs.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/Do69D0sNlG9eqr9cyE7bm.png)
+![](Bin.png)