Zephyrus-H/SMP_FSAVC_Dataset

SMP-FSAVC splits and semantic prompts

中文说明 · SMP code · Paper · DOI

This repository releases the source/target class partitions, train/test sample lists, and mPLUG-2-generated semantic prompts used by Semantic Modulated Prompting for Few-Shot Audio-Visual Classification (SMP-FSAVC). It is ready to host on both GitHub and the Hugging Face Hub: the original headerless CSV files are preserved for the training code, while equivalent Parquet files power the Hugging Face Dataset Viewer.

No video, audio, or extracted frame is distributed here. Users must obtain media from the respective upstream datasets and follow their terms.

What is included

Dataset	Released rows	Source classes	Target classes	Notes
AVE	4,051	16	12	28 defined classes
Kinetics-Sounds	22,908	19	13	32 defined classes
VGGSound100	59,795	60 defined / 59 available	40	Source label 14 has no obtainable media in this snapshot
Total	86,754

The VGGSound100 files named train_caption.csv and test_caption.csv in the research workspace are not included because they are exact unions of the four released split files and would duplicate content.

Split semantics

File	Hugging Face split	Meaning
pretrain.csv	source_train	Source-class pretraining set
pretrain_test.csv	source_test	Source-class evaluation set
fewshot.csv	target_train_pool	Target-class pool from which N-way K-shot support sets are sampled
fewshot_test.csv	target_test	Target-class query/evaluation set

fewshot.csv is a sampling pool, not one fixed K-shot episode. The SMP protocol repeatedly samples support examples from it.

Repository layout

.
|-- csv/                       # original headerless files used by SMP
|   |-- AVE/
|   |-- Kinetics-Sounds/
|   `-- VGGSound100/
|-- viewer/                    # equivalent Parquet files for Dataset Viewer
|-- metadata/
|   |-- dataset_statistics.json
|   |-- generation_config.yaml
|   |-- label_maps.json
|   `-- checksums.sha256
|-- scripts/
|   |-- build_release.py
|   `-- verify_release.py
|-- LICENSE_DATA.md
|-- LICENSE_CODE
`-- DATA_REMOVAL.md

Data schema

The raw files have no header. AVE rows contain three fields:

clip_id,integer_label,semantic_prompt

Kinetics-Sounds and VGGSound100 rows contain a fourth field:

clip_id,integer_label,semantic_prompt,class_name

CSV quoting is used where captions or class names contain commas. The Parquet mirrors expose the same four named columns for every dataset:

Column	Type	Description
clip_id	string	Upstream clip identifier; no media URL or media payload
label	int64	Dataset-specific integer class label
semantic_prompt	string	mPLUG-2-generated video caption used as the semantic prompt
class_name	string	Human-readable class name; derived from the AVE identifier suffix for AVE

Complete source/target label mappings and availability flags are in metadata/label_maps.json.

Loading the data

The Parquet files require no custom loading script. After cloning this repository:

from datasets import load_dataset

ave = load_dataset(
    "parquet",
    data_files={
        "source_train": "viewer/AVE/pretrain.parquet",
        "source_test": "viewer/AVE/pretrain_test.parquet",
        "target_train_pool": "viewer/AVE/fewshot.parquet",
        "target_test": "viewer/AVE/fewshot_test.parquet",
    },
)

When this tree is pushed to a Hugging Face dataset repository, the YAML configuration at the top of this card automatically exposes the ave, kinetics-sounds, and vggsound100 configurations in Dataset Viewer.

For the SMP training repository, point the source annotation root at a dataset's csv/<dataset>/ directory for pretraining. Point the few-shot root at the same directory for target training; the scripts select the required filenames.

Semantic prompts

The released semantic_prompt values were generated from visual frames with mPLUG-2, using its MSVD-finetuned video captioning checkpoint. The retained settings use 16 RGB frames at 224×224, ViT-L/14, beam size 5, output lengths 4–20, and seed 42. See metadata/generation_config.yaml for the full reconstructed configuration and reproducibility caveats.

SMP also supports static prompts. To test that setting, replace each sample's prompt with:

static_prompt = "a video of [label]"

[label] is literal text: it is not substituted with the sample label or class name. Every sample receives exactly the same string.

The published CSVs retain the generated captions; the static alternative is not duplicated as another set of files.

Integrity and rebuilding

Verify the checked-in release artifacts with Python's standard library:

python scripts/verify_release.py

To validate the raw CSVs and rebuild all Parquet mirrors and metadata:

python -m pip install -r requirements-build.txt
python scripts/build_release.py

The build script checks row counts, column counts, blank fields, duplicate clip IDs, label coverage, class-name consistency, and leakage across the four files. It never rewrites the original CSVs.

Limitations and responsible use

• Semantic prompts are model-generated descriptions, not ground-truth captions. They may hallucinate, omit audible/visible events, encode social biases, or disagree with the class label.
• The released sample counts reflect the processed media snapshot available during the research. YouTube-hosted source clips can disappear over time, so later downloads may not reproduce the same media inventory.
• VGGSound100 source label 14 (subway, metro) has no available example in this release because the original videos could not be obtained. The numeric label space remains 0..59 for compatibility.
• The original frame loader did not explicitly sort stored frame filenames before uniform selection. The retained settings therefore document the procedure but do not guarantee bit-for-bit caption regeneration on a new filesystem snapshot.
• These annotations are intended for research on audio-visual learning and few-shot classification. Inspect generated captions and upstream media for suitability before using them in sensitive applications.

See DATA_REMOVAL.md for correction or removal requests.

Licensing

The release contains derived annotations and identifiers, but no media. See LICENSE_DATA.md for the layered licensing terms. In short, release-authored split definitions, generated prompts, and metadata are provided under CC BY 4.0 to the extent the authors hold rights; upstream identifiers and labels remain subject to their source terms, and video copyright remains with the original owners.

Citation

If you use these splits or semantic prompts, please cite the SMP article and the upstream datasets relevant to your experiment.

@ARTICLE{11352954,
  author={Huang, Guanjie and Cui, Yawen and Tsang, Danny H.K. and Wang, Wenwu and Liu, Li},
  journal={IEEE Transactions on Audio, Speech and Language Processing},
  title={Semantic Modulated Prompting for Few-Shot Audio-Visual Classification},
  year={2026},
  volume={34},
  pages={723-736},
  doi={10.1109/TASLPRO.2026.3654246}
}

Additional BibTeX entries for AVE, Kinetics/Kinetics-Sounds, VGGSound, and mPLUG-2 are provided below.

Upstream dataset and caption-model citations

@inproceedings{tian2018ave,
  author={Tian, Yapeng and Shi, Jing and Li, Bochen and Duan, Zhiyao and Xu, Chenliang},
  title={Audio-Visual Event Localization in Unconstrained Videos},
  booktitle={European Conference on Computer Vision},
  pages={247--263},
  year={2018}
}

@inproceedings{carreira2017quo,
  author={Carreira, Joao and Zisserman, Andrew},
  title={Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6299--6308},
  year={2017}
}

@inproceedings{arandjelovic2017look,
  author={Arandjelovic, Relja and Zisserman, Andrew},
  title={Look, Listen and Learn},
  booktitle={IEEE International Conference on Computer Vision},
  pages={609--617},
  year={2017}
}

@inproceedings{chen2020vggsound,
  author={Chen, Honglie and Xie, Weidi and Vedaldi, Andrea and Zisserman, Andrew},
  title={VGGSound: A Large-Scale Audio-Visual Dataset},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing},
  pages={721--725},
  year={2020}
}

@inproceedings{xu2023mplug2,
  author={Xu, Haiyang and Ye, Qinghao and Yan, Ming and Shi, Yaya and Ye, Jiabo and Xu, Yuanhong and Li, Chenliang and Bi, Bin and Qian, Qi and Wang, Wei and Xu, Guohai and Zhang, Ji and Huang, Songfang and Huang, Fei and Zhou, Jingren},
  title={mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video},
  booktitle={Proceedings of the 40th International Conference on Machine Learning},
  volume={202},
  pages={38728--38748},
  year={2023}
}