RAVEN: Query-Guided Representation Alignment for Question Answering over Audio, Video, Embedded Sensors, and Natural Language
Project Page: https://bashlab.github.io/raven_project/ • Code: https://github.com/BASHLab/RAVEN
Abstract
Multimodal question answering (QA) often requires identifying which video, audio, or sensor tokens are relevant to the question. Yet modality disagreements are common: off-camera speech, background noise, or motion outside the field of view often mislead fusion models that weight all streams equally. We present RAVEN, a unified QA architecture whose core is QuART, a query-conditioned cross-modal gating module that assigns scalar relevance scores to each token across modalities, enabling the model to amplify informative signals and suppress distractors before fusion. RAVEN is trained through a three-stage pipeline comprising unimodal pretraining, query-aligned fusion, and disagreement-oriented fine-tuning -- each stage targeting a distinct challenge in multi-modal reasoning: representation quality, cross-modal relevance, and robustness to modality mismatch. To support training and evaluation, we release AVS-QA, a dataset of 300K synchronized Audio--Video-Sensor streams paired with automatically generated question-answer pairs. Experimental results on seven multi-modal QA benchmarks -- including egocentric and exocentric tasks -- show that RAVEN achieves up to 14.5% and 8.0% gains in accuracy compared to state-of-the-art multi-modal large language models, respectively. Incorporating sensor data provides an additional 16.4% boost, and the model remains robust under modality corruption, outperforming SOTA baselines by 50.23%. Our code and dataset are available at this https URL .
🚀 Main Results
Comparison of RAVEN and prior MLLMs on exocentric open-ended video QA (MSVD-QA, MSRVTT-QA, ActivityNet-QA) and audio-visual QA (AVSD, MUSIC-QA) benchmarks. Best and second-best scores are in $\textbf{Bold}$ and $\underline{\text{underline}}$. $^*$ indicates scores reproduced by us.
Comparison of RAVEN with MLLMs on the EgoThink (Reasoning) and AVS-QA benchmarks. RAVEN outperforms across metrics and excels in reasoning. $\textbf{Bold}$ and $\underline{\text{underline}}$ indicate the best and second-best scores.
📁 AVS-QA Dataset
Train and test split of AVS-QA is provided here.
More details here.
🛠️ Requirements and Installation
Basic Dependencies:
- Python >= 3.8
- Pytorch >= 2.2.0
- CUDA Version >= 11.8
- transformers == 4.40.0 (for reproducing paper results)
- tokenizers == 0.19.1
cd RAVEN
pip install -r requirements.txt
pip install flash-attn==2.5.8 --no-build-isolation
pip install opencv-python==4.5.5.64
apt-get update && apt-get install ffmpeg libsm6 libxext6 -y
🍀 Model Zoo
| Model Name | Modal Type |
|---|---|
| RAVEN-7B-AV | AV |
| RAVEN-7B-AVS | AVS |
🤖 Sample Usage
- STEP 1: Download $\texttt{siglip-so400m-patch14-384}$ from here google/siglip-so400m-patch14-384
- STEP 2: Download RAVEN checkpoint
CUDA_VISIBLE_DEVICES=0 python inference.py --model-path=<MODEL PATH> --modal-type=<MODAL TYPE>
👍 Acknowledgement
The codebase of RAVEN is adapted from VideoLLaMA2. We are also grateful for their contribution.
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