metadata

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

🏠 Project Page | 📄 arXiv Paper | 📦 Dataset | 🏅Leaderboard

StreamingBench evaluates Multimodal Large Language Models (MLLMs) in real-time, streaming video understanding tasks. 🌟

🎞️ 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

🔍 Task Taxonomy

🔬 Experimental Results

Performance of Various MLLMs on StreamingBench

All Context
60 seconds of context preceding the query time
Comparison of Main Experiment vs. 60 Seconds of Video Context

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.

📝 Citation

@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