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README.md

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+---
+task_categories:
+- question-answering
+language:
+- en
+size_categories:
+- 1K<n<10K
+
+
+dataset_info:
+  - config_name: Real_Time_Visual_Understanding
+    features:
+      - name: question_id
+        dtype: string
+      - name: task_type
+        dtype: string
+      - name: question
+        dtype: string
+      - name: time_stamp
+        dtype: string
+      - name: answer
+        dtype: string
+      - name: options
+        dtype: string
+      - name: frames_required
+        dtype: string
+      - name: temporal_clue_type
+        dtype: string
+    splits:
+      - name: Real_Time_Visual_Understanding
+        num_examples: 2500
+
+  - config_name: Sequential_Question_Answering
+    features:
+      - name: question_id
+        dtype: string
+      - name: task_type
+        dtype: string
+      - name: question
+        dtype: string
+      - name: time_stamp
+        dtype: string
+      - name: answer
+        dtype: string
+      - name: options
+        dtype: string
+      - name: frames_required
+        dtype: string
+      - name: temporal_clue_type
+        dtype: string
+    splits:
+      - name: Sequential_Question_Answering
+        num_examples: 250
+
+
+  - config_name: Contextual_Understanding
+    features:
+      - name: question_id
+        dtype: string
+      - name: task_type
+        dtype: string
+      - name: question
+        dtype: string
+      - name: time_stamp
+        dtype: string
+      - name: answer
+        dtype: string
+      - name: options
+        dtype: string
+      - name: frames_required
+        dtype: string
+      - name: temporal_clue_type
+        dtype: string
+    splits:
+      - name: Contextual_Understanding
+        num_examples: 500
+
+  - config_name: Omni_Source_Understanding
+    features:
+      - name: question_id
+        dtype: string
+      - name: task_type
+        dtype: string
+      - name: question
+        dtype: string
+      - name: time_stamp
+        dtype: string
+      - name: answer
+        dtype: string
+      - name: options
+        dtype: string
+      - name: frames_required
+        dtype: string
+      - name: temporal_clue_type
+        dtype: string
+    splits:
+      - name: Omni_Source_Understanding
+        num_examples: 1000
+
+    
+  - config_name: Proactive_Output
+    features:
+      - name: question_id
+        dtype: string
+      - name: task_type
+        dtype: string
+      - name: question
+        dtype: string
+      - name: time_stamp
+        dtype: string
+      - name: ground_truth_time_stamp
+        dtype: string
+      - name: ground_truth_output
+        dtype: string
+      - name: frames_required
+        dtype: string
+      - name: temporal_clue_type
+        dtype: string
+    splits:
+      - name: Proactive_Output
+        num_examples: 250
+
+
+configs:
+  - config_name: Real_Time_Visual_Understanding
+    data_files:
+    - split: Real_Time_Visual_Understanding
+      path: StreamingBench/Real_Time_Visual_Understanding.csv 
+      
+  - config_name: Sequential_Question_Answering
+    data_files:
+    - split: Sequential_Question_Answering
+      path: StreamingBench/Sequential_Question_Answering.csv
+
+  - config_name: Contextual_Understanding
+    data_files:
+    - split: Contextual_Understanding
+      path: StreamingBench/Contextual_Understanding.csv
+      
+  - config_name: Omni_Source_Understanding
+    data_files:
+    - split: Omni_Source_Understanding
+      path: StreamingBench/Omni_Source_Understanding.csv
+      
+  - config_name: Proactive_Output
+    data_files:
+    - split: Proactive_Output
+      path: StreamingBench/Proactive_Output_50.csv
+    - split: Proactive_Output_250
+      path: StreamingBench/Proactive_Output.csv
+    
+---
+# StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding
+
+<div align="center">
+  <img src="./figs/icon.png" width="100%" alt="StreamingBench Banner">
+
+  <div style="margin: 30px 0">
+    <a href="https://streamingbench.github.io/" style="margin: 0 10px">🏠 Project Page</a> |
+    <a href="https://arxiv.org/abs/2411.03628" style="margin: 0 10px">📄 arXiv Paper</a> |
+    <a href="https://huggingface.co/datasets/mjuicem/StreamingBench" style="margin: 0 10px">📦 Dataset</a> |
+    <a href="https://streamingbench.github.io/#leaderboard" style="margin: 0 10px">🏅Leaderboard</a>
+  </div>
+</div>
+
+**StreamingBench** evaluates **Multimodal Large Language Models (MLLMs)** in real-time, streaming video understanding tasks. 🌟
+
+------
+
+[**NEW!** 2025.05.15] 🔥: [Seed1.5-VL](https://github.com/ByteDance-Seed/Seed1.5-VL) achieved ALL model SOTA with a score of 82.80 on the Proactive Output.
+
+[**NEW!** 2025.03.17] ⭐: [ViSpeeker](https://arxiv.org/abs/2503.12769) achieved Open-Source SOTA with a score of 61.60 on the Omni-Source Understanding.
+
+[**NEW!** 2025.01.14] 🚀: [MiniCPM-o 2.6](https://github.com/OpenBMB/MiniCPM-o) achieved Streaming SOTA with a score of 66.01 on the Overall benchmark.
+
+[**NEW!** 2025.01.06] 🏆: [Dispider](https://github.com/Mark12Ding/Dispider) achieved Streaming SOTA with a score of 53.12 on the Overall benchmark.
+
+[**NEW!** 2024.12.09] 🎉: [InternLM-XComposer2.5-OmniLive](https://github.com/InternLM/InternLM-XComposer) achieved 73.79 on Real-Time Visual Understanding.
+
+------
+
+## 🎞️ Overview
+
+As MLLMs continue to advance, they remain largely focused on offline video comprehension, where all frames are pre-loaded before making queries. However, this is far from the human ability to process and respond to video streams in real-time, capturing the dynamic nature of multimedia content. To bridge this gap, **StreamingBench** introduces the first comprehensive benchmark for streaming video understanding in MLLMs.
+
+### Key Evaluation Aspects
+- 🎯 **Real-time Visual Understanding**: Can the model process and respond to visual changes in real-time?
+- 🔊 **Omni-source Understanding**: Does the model integrate visual and audio inputs synchronously in real-time video streams?
+- 🎬 **Contextual Understanding**: Can the model comprehend the broader context within video streams?
+
+### Dataset Statistics
+- 📊 **900** diverse videos
+- 📝 **4,500** human-annotated QA pairs
+- ⏱️ Five questions per video at different timestamps
+#### 🎬 Video Categories
+<div align="center">
+  <img src="./figs/StreamingBench_Video.png" width="80%" alt="Video Categories">
+</div>
+
+#### 🔍 Task Taxonomy
+<div align="center">
+  <img src="./figs/task_taxonomy.png" width="80%" alt="Task Taxonomy">
+</div>
+
+
+## 🔬 Experimental Results
+
+### Performance of Various MLLMs on StreamingBench
+- All Context
+<div align="center">
+  <img src="./figs/result_1.png" width="80%" alt="Task Taxonomy">
+</div>
+
+- 60 seconds of context preceding the query time
+<div align="center">
+  <img src="./figs/result_2.png" width="80%" alt="Task Taxonomy">
+</div>
+
+- Comparison of Main Experiment vs. 60 Seconds of Video Context
+- <div align="center">
+  <img src="./figs/heatmap.png" width="80%" alt="Task Taxonomy">
+</div>
+
+### Performance of Different MLLMs on the Proactive Output Task
+*"≤ xs" means that the answer is considered correct if the actual output time is within x seconds of the ground truth.*
+<div align="center">
+  <img src="./figs/po.png" width="80%" alt="Task Taxonomy">
+</div>
+
+
+## 📝 Citation
+```bibtex
+@article{lin2024streaming,
+  title={StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding},
+  author={Junming Lin and Zheng Fang and Chi Chen and Zihao Wan and Fuwen Luo and Peng Li and Yang Liu and Maosong Sun},
+  journal={arXiv preprint arXiv:2411.03628},
+  year={2024}
+}
+```
+
+https://arxiv.org/abs/2411.03628

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