We present an efficient encoder-free approach for video-language understanding that achieves competitive performance while significantly reducing computational overhead. Current video-language models typically rely on heavyweight image encoders (300M-1.1B parameters) or video encoders (1B-1.4B parameters), creating a substantial computational burden when processing multi-frame videos. Our method introduces a novel Spatio-Temporal Alignment Block (STAB) that directly processes video inputs without requiring pre-trained encoders while using only 45M parameters for visual processing - at least a 6.5x reduction compared to traditional approaches. The STAB architecture combines Local Spatio-Temporal Encoding for fine-grained feature extraction, efficient spatial downsampling through learned attention and separate mechanisms for modeling frame-level and video-level relationships. Our model achieves comparable or superior performance to encoder-based approaches for open-ended video question answering on standard benchmarks. The fine-grained video question-answering evaluation demonstrates our model's effectiveness, outperforming the encoder-based approaches Video-ChatGPT and Video-LLaVA in key aspects like correctness and temporal understanding. Extensive ablation studies validate our architectural choices and demonstrate the effectiveness of our spatio-temporal modeling approach while achieving 3-4x faster processing speeds than previous methods.
Model Weights
We release the pretrained and instruction-tuned weights of Video-Panda in this repository.
✒️ Citation
If Video-Panda is helpful for your research, please consider star ⭐ and citation 📝 :
@article{yi2024video-panda,
author = {Jinhui Yi* and Syed Talal Wasim* and Yanan Luo* and Muzammal Naseer and Juergen Gall},
title = {Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models},
journal = {arXiv preprint, arXiv:2412.18609},
year = {2024},
}
