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base_model: meta-llama/Llama-2-7b-chat-hf
library_name: peft
license: cc-by-nc-sa-4.0
tags:
- audio
- video
- segmentation
- mask-quality-assessment
- audio-visual-segmentation
- lora
---
# MQ-Auditor HyperLoRA Weights
This repository contains the released MQ-Auditor pretrained weights for reference-free mask quality assessment in language-referred audio-visual segmentation.
The checkpoint corresponds to:
```text
epochs96_lr1e-4_bs4_gradacc8_lora_r32alpha64_pos0.5_ioulosswei0
```
## Model
MQ-Auditor takes a video clip, audio, a referring expression, a frame, and a candidate segmentation mask, then predicts mask quality attributes such as mask type, IoU, and recommended action.
The released weights are intended to be used with the MQ-Auditor codebase and MQ-RAVSBench dataset. The base LLM checkpoint and external encoders are not included in this package.
## Release Contents
The public weight package should include:
```text
adapter_config.json
adapter_model.safetensors
config.json
model.txt
model_trainable_params.txt
non_lora_trainables.bin
saved_config.json
trainer_state.json
checkpoint-960/
config.json
finetune_weights.bin
```
Intermediate epoch checkpoints and TensorBoard logs are not part of the release package.
## Training Data
The model was trained on MQ-RAVSBench with:
```text
train_test_meta_files/metadata.csv
train_test_meta_files/train_audit_only_filtered.json
```
`null` masks are used during training as empty-mask examples. They are not part of the default/reported test-time evaluation protocol.
## Evaluation
Evaluation is reported on the seen and unseen MQ-RAVSBench test splits:
```text
test_s_image_filtered.json
test_u_image_filtered.json
test_s_video_filtered.json
test_u_video_filtered.json
```
Reported mask types focus on non-empty candidate masks: `perfect`, `cutout`, `erode`, `dilate`, `merge`, and `full_neg`.
## License
The released MQ-Auditor weights are provided for non-commercial research purposes only under CC BY-NC-SA 4.0-style terms. The weights depend on the Llama-2 base model and other pretrained encoders, so users must also comply with the applicable upstream model licenses and access terms.
## Citation
```bibtex
@article{zhou2026audit,
title={Audit After Segmentation: Reference-Free Mask Quality Assessment for Language-Referred Audio-Visual Segmentation},
author={Zhou, Jinxing and Zhou, Yanghao and Wang, Yaoting and Han, Zongyan and Ma, Jiaqi and Ding, Henghui and Anwer, Rao Muhammad and Cholakkal, Hisham},
journal={arXiv preprint arXiv:2602.03892},
year={2026}
}
```
Paper: https://arxiv.org/pdf/2602.03892
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