SiLVR : A Simple Language-based Video Reasoning Framework

Ce Zhang*, Yan-Bo Lin*, Ziyang Wang, Mohit Bansal, Gedas Bertasius

   Paper    |    Project Page   

Updates

• [05/28/2025] Code Release.

Introduction

We present SiLVR, a Simple Language-based Video Reasoning framework that decomposes complex video understanding into two stages.

• In the first stage, we convert raw videos into rich language-based descriptions. Specifically, we densely sample short clips from the input videos and use a pre-trained visual captioner (e.g., NVILA) to extract captions for each clip. Additionally, we use automatic speech recognition (ASR) tools to convert speech into language descriptions.
• In the second stage, we feed the rich language descriptions into a strong reasoning LLM (e.g. DeepSeek-R1) to solve complex video-language understanding tasks.

SiLVR offers several benefits:

• Simplicity: SiLVR does not require complex RL-based optimization or specialized modules for different tasks.
• Generalizability: SiLVR can be applied to a wide range of complex video-language tasks without task-specific fine-tuning.
• Modularity: SiLVR's modular design enables seamless use of powerful visual captioning models and strong reasoning LLMs.
• Flexibility: SiLVR supports plug-and-play integration of different captioning models, speech recognition models, and LLMs. An overview of our method is illustrated in Figure 2.
• Strong Performance: SiLVR achieves state-of-the-art results on multiple VideoQA benchmarks, including Video-MME (long), Video-MMMU (comprehension), Video-MMLU (quiz), CGBench, and EgoLife.

We believe the simple yet effective design of SiLVR will enable the research community to build on our work and use our simple framework as a baseline to develop even more powerful video-language reasoning models.

Installation

conda create --name=silvr python=3.9
conda activate silvr

git clone .
cd .
pip install -r requirements.txt

Data Preparation

Download the caption and subtitle files for each dataset from here: https://drive.google.com/file/d/13L1Y1hr6aMoxarGxhN1QXY962y7ClORd/view?usp=drive_link. Unzip and move the caption and subtitle files to ./data:

SILVR/
├── data/           # Directory for input data
│   ├── videomme/
│   |  ├── subtitles/
│   |     ├── _8lBR0E_Tx8.srt
│   |     ├── ... 
│   |     └── ZXoaMa6jlO4.srt
│   |  ├── captions_1s/
│   |     ├── _8lBR0E_Tx8.txt
│   |     ├── ... 
│   |     └── ZXoaMa6jlO4.txt
│   |  ├── captions_8s/
│   |  ├── captions_64s/
│   |  ├── captions_64s_qwen7b/
│   |  └── captions_64s_qwen72b/
│   ├── videommmu/
│   ├── videommlu/
│   ├── cgbench/
│   ├── cinepile/
│   ├── mmvu/
│   ├── mmworld/
│   ├── egolife/
│   └── hourvideo/
├── output/         # (Empty) Directory for output files
├── eval/           # Evaluation scripts for different benchmarks
├── main.py
├── dataset.py      # Dataset loading and processing utilities
├── model.py
├── prompts.py      # Prompt templates
└── utils.py

Download Video-MMLU annotations (https://huggingface.co/datasets/Enxin/Video-MMLU) to data/videommlu/Video-MMLU.

Download Video-MMLU category file (https://huggingface.co/datasets/Enxin/Video-MMLU/blob/main/video_sources.jsonl) to data/videommlu/Video-MMLU/video_sources.jsonl.

Download HourVideo dev set annotations (https://huggingface.co/datasets/HourVideo/HourVideo/viewer/default/dev) to data/hourvideo/HourVideo.

Download EgoLife annotations (https://huggingface.co/datasets/lmms-lab/EgoLife/tree/main) to data/egolife/EgoLife.

Experiments

We provide the output files here: https://drive.google.com/file/d/13PmRQsu71XUJ7UyMqjJBfJUBtlfMl0V5/view?usp=drive_link.

Feel free to download and check the raw outputs of our methods.

Setup

Please prepare the following API keys:

• $API_KEY: the target API key that you want to use. This can be DeepSeek, OpenAI, Lambda, etc.
• $API_URL: the API URL which the code makes request to. By default, it is https://api.deepseek.com/v1/chat/completions pointing to DeepSeek.
• $HF_TOKEN: your Hugging Face login token. This is used for retriving dataset annotations from Huggface Face.
• $OPENAI_API_KEY: OpenAI API key. This is only needed for MMVU which needs GPT-4 for open-ended question evaluation.

Inference and Evaluation

We organized inference and evaluation scripts for all datasets in ./scripts. The datasets include CGBench, CinePile, EgoLife, HourVideo, MMVU, MMWorld, Video-MME, Video-MMLU, Video-MMMU.

Use Local LLMs for Inference

By default, we use API services for LLM inference. If you want to use local LLMs, make sure to set --single_process to disable multi-processing.

Debug

• --num_examples_to_run: how many examples to run.
• --single_process: disable multi-processing.

Visualization

Citation

If our work is useful to your research, please consider citing it.

@article{zhang2025silvr,
  title={SiLVR: A Simple Language-based Video Reasoning Framework},
  author={Zhang, Ce and Lin, Yan-Bo and Wang, Ziyang and Bansal, Mohit and Bertasius, Gedas},
  year={2025},
  journal={arXiv preprint arXiv:2505.24869},
}