MQA
Collection
mask quality assessment for referring audio-visual segmentation • 2 items • Updated
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: ReadTimeout
Message: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: fdd3e962-dcaf-4340-b015-47f284abd56f)')
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1192, in dataset_module_factory
).get_module()
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 608, in get_module
standalone_yaml_path = cached_path(
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 180, in cached_path
).resolve_path(url_or_filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
self._api.repo_info(
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 2816, in repo_info
return method(
^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 2673, in dataset_info
r = get_session().get(path, headers=headers, timeout=timeout, params=params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 602, in get
return self.request("GET", url, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
return super().send(request, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/adapters.py", line 690, in send
raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: fdd3e962-dcaf-4340-b015-47f284abd56f)')Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MQ-RAVSBench
MQ-RAVSBench is a benchmark for mask-quality auditing in referring audio-visual segmentation. Each example links a video clip, audio, a referring expression, the ground-truth object mask, and candidate masks with different error patterns. The benchmark is used by MQ-Auditor to assess whether a candidate mask should be accepted, revised, or rejected.
All paths stored in the metadata files are relative to the dataset root.
Dataset Layout
MQ-RAVSBench/
README.md
media/
<vid>/
audio.wav
frames/
0.jpg ... 9.jpg
gt_mask/
<vid>/fid_<fid>/<frame>.png
part_neg_masks/
<save_id>/<frame>/{cutout,erode,dilate,merge}/...
full_neg_masks/
<save_id>/<frame>/<category>/...
null_masks/
<save_id>/<frame>/000.png
train_test_meta_files/
metadata.csv
train_audit_only_filtered.json
test_s_image_filtered.json
test_u_image_filtered.json
test_s_video_filtered.json
test_u_video_filtered.json
Directory summary for this release:
| Directory | Contents |
|---|---|
media/ |
1,840 clips, each with audio.wav and 10 extracted frames |
gt_mask/ |
Ground-truth segmentation masks (perfect) |
part_neg_masks/ |
Partially incorrect masks, including cutout, erode, dilate, and merge errors |
full_neg_masks/ |
Masks of non-target objects (full_neg) |
null_masks/ |
Empty masks used during MQ-Auditor training (null) |
train_test_meta_files/ |
CSV and JSON metadata used by training and evaluation scripts |
Metadata
train_test_meta_files/metadata.csv contains the base sample metadata:
| Column | Meaning |
|---|---|
vid |
Clip id. Matches a folder under media/ |
uid |
Query/object instance id |
split |
Split label from the source metadata |
fid |
Object/mask id used in gt_mask/<vid>/fid_<fid>/... |
exp |
Referring expression |
kfid |
Key-frame index used by image-mode evaluation |
Split counts in metadata.csv:
| Split | Count |
|---|---|
train |
1,306 |
test_s |
437 |
test_u |
303 |
The default MQ-Auditor training and evaluation scripts use these JSON files:
| File | Entries | Usage |
|---|---|---|
train_audit_only_filtered.json |
1,306 | Supervised fine-tuning data with audit responses |
test_s_image_filtered.json |
437 | Seen-category image/key-frame evaluation |
test_u_image_filtered.json |
303 | Unseen-category image/key-frame evaluation |
test_s_video_filtered.json |
60 | Seen-category video evaluation |
test_u_video_filtered.json |
40 | Unseen-category video evaluation |
Candidate mask types include perfect, cutout, erode, dilate, merge, full_neg, and null. Candidate entries provide the mask path, IoU to the ground-truth mask, and the recommended audit action when available.
null masks are used when training MQ-Auditor, but they are not part of the default/reported test protocol. In our experiments, the trained auditor can identify this mask type perfectly, so test-time evaluation focuses on the non-empty candidate masks.
Code and Pretrained Weights
The MQ-Auditor source code, training scripts, inference scripts, and pretrained weights are released separately from MQ-RAVSBench: https://github.com/jasongief/MQA-RefAVS
The released MQ-Auditor pretrained checkpoint corresponds to:
epochs96_lr1e-4_bs4_gradacc8_lora_r32alpha64_pos0.5_ioulosswei0
See the MQ-Auditor code release for training and evaluation commands.
License
MQ-RAVSBench is licensed under a CC BY-NC-SA 4.0 License and is released for non-commercial research purposes only. MQ-RAVSBench incorporates videos and/or annotations from previous datasets, including Ref-AVS and AVSBench; users must also comply with the licenses and terms of the original datasets.
Citation
If you use MQ-RAVSBench or MQ-Auditor, please cite:
@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}
}
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