license: cc-by-nc-nd-4.0
library_name: peft
base_model: lmms-lab/LLaVA-Video-7B-Qwen2
tags:
- video-understanding
- long-video
- visual-question-answering
- lora
- qwen2
- llava
- cvpr2026
datasets:
- lmms-lab/LLaVA-Video-178K
pipeline_tag: video-text-to-text
QViC-MF-7B
This is the official model for the paper "Question-guided Visual Compression with Memory Feedback for Long-Term Video Understanding" (CVPR 2026).
- Paper: arXiv
- Code: GitHub
- Authors: Sosuke Yamao*, Natsuki Miyahara*, Yuankai Qi, Shun Takeuchi
- Affiliation: Fujitsu Research / Macquarie University
Model Description
QViC-MF-7B is a LoRA adapter trained on top of LLaVA-Video-7B-Qwen2 for long-term video understanding. It introduces Question-guided Multimodal Selective Attention (QMSA), which learns to preserve visual information related to the given question from both the current clip and past related frames stored in a context memory. The compressor and memory feedback work iteratively for each clip of the entire video, enabling strong performance on tasks that require understanding complete events (e.g., temporal ordering).
How to Use
Please refer to the official code repository for inference and evaluation scripts.
Model Details
| Property | Value |
|---|---|
| Base Model | lmms-lab/LLaVA-Video-7B-Qwen2 |
| Architecture | LlavaQwenForCausalLM |
| Vision Encoder | SigLIP-SO400M (google/siglip-so400m-patch14-384) |
| LLM | Qwen2-7B |
| Adapter | LoRA (r=64, alpha=16) |
| Context Memory Length | 256 tokens |
| Precision | bfloat16 |
Citation
@inproceedings{yamao2026qvicmf,
title={Question-guided Visual Compression with Memory Feedback for Long-Term Video Understanding},
author={Yamao, Sosuke and Miyahara, Natsuki and Qi, Yuankai and Takeuchi, Shun},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2026}
}
License
This model is released under the CC-BY-NC-ND-4.0 license.