📝 Highlights

🚀 Introducing OVBench OVBench is a benchmark tailored for real-time video understanding:

• Memory, Perception, and Prediction of Temporal Contexts: Questions are framed to reference the present state of entities, requiring models to memorize/perceive/predict past/present/future temporal contexts over time.
• Dynamic Spatio-temporal Interaction: The benchmark demands precise real-time interactions with video content, where actions, objects, and events must be understood in the context of their spatial and temporal relationships.
• Contextual Awareness at Specific Moments: Real-time questions are contextual, changing based on the specific timestamp they are asked, requiring a deep understanding of how temporal context evolves.

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🏗️ Pyramid Memory Bank

To tackle the challenges of infinite video streams, we propose a multi-layered Pyramid Memory Bank that balances spatial and temporal information:

1. Spatial Anchors: The lower layers retain high-resolution features to preserve fine-grained spatial cues, capturing keyframes as "spatial anchors" with a lower sampling rate.
2. Progressive Abstraction: As the layers progress, spatial resolution decays while the temporal sampling rate grows proportionally, forming an abstract representation of fine-grained long-short-term patterns.
3. Dynamic Eviction: A dynamic eviction mechanism detects temporal redundancy via similarity, combined with pooling for spatial compression, improving storage efficiency.

NOTE: The currently open-sourced code implementation of ours constructs memory based on ViT features. We will provide the implementation that builds memory using KV-Cache features in subsequent updates.

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🎯 Offline-to-Online Learning Paradigm

A novel training strategy designed for online video streams:

• Interleaved Dialogue Tuning: Combines offline video data with online instruction tuning in a dialogue format.
• Progressive Learning: Bridges offline and online video understanding, enhancing real-time adaptability.

To-Do

• Model checkpoint Upload
• A more interactive demo

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🏆 OVBench Leaderboard

See our leaderboard here

Evaluate your model

Evaluation of Existing Models on OVBench Using lmms_eval.

Preparatory Steps

• Environment Setup: Ensure that all dependencies required by lmms_eval are properly installed.

• Please perform a global search for the field /path_to_your in the ./lmms-eval-ovbench directory and replace it with the corresponding file path on your local system.

Predefined Model Evaluation

• Execute the script ./lmms-eval-ovbench/scripts/eval_models/eval_internvl2-8B.sh to initiate the benchmark evaluation.

Custom Model Evaluation

• Given that the video data used in this benchmark consists of both image sequences and video clips, it is necessary to utilize the ./lmms-eval-ovbench/llava/video_utils.py to read video data correctly.

• You may refer to the implementation of the load_video function in ./lmms-eval-ovbench/lmms_eval/models/internvl2.py as a guideline. Integrate this function into your custom model as needed to enable compatibility with the lmms_eval evaluation framework.

Submit the results

Email xinhaoli00@outlook.com with your result.json or open an issue in this repo.

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🎥 Demo

To launch the demo, use the following script:

https://github.com/user-attachments/assets/4cdc3e57-3dae-4656-8366-a10cc6648884

bash gradio_demo.sh

🛠️ Installation

To install the necessary dependencies, use the following commands:

conda create -n your_env python=3.9
pip install -r requirements.txt

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📦 Offline Data Preparation

The anno_data file provides the paths for different types of datasets:

"coin_sl_train": {
    "annotation": "Path to the annotations json file.",
    "data_root": "your data path",
},
...

We support the data reading formats LLaVA and VideoChat2-IT for specific data JSON formats.

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🔄 Online SFT Data Download

For the construction format of online data, please refer to VideoChatOnline-IT

📈 Evaluations Results of VideoChatOnline-4B on Long Video Benchmarks

Benchmark	Result
OVBench	54.9
VideoMME	Short: 65.8 Medium: 50.2 Long: 47.1 Avg: 54.4
MVBench	65.2
EgoSchema	54.7
MLVU	60.8
LongVideoBench	54.1

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🚀 Training

To run the training, execute the following bash commands for different stages:

#Offline SFT:
bash shell/online_4b/videochat_online_4b_stage1_ft.sh

#Online & Offline Joint SFT:
bash shell/online_4b/videochat_online_4b_stage2_ft.sh

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📊 Evaluation on OVBench

#Sliding Window Setting:
bash shell/eval/online_bench_sliding_window.sh
#Streaming Setting:
bash shell/eval/online_bench_stream.sh