Rowance/HSA-features

HSA Frozen Features

Paper · Code

Precomputed ImageBind and LanguageBind features for the main experiments of Hub-Spectral Activation of Latent Multimodal Knowledge. With them you can reproduce the paper's HSA and frozen-cosine results without downloading the original datasets or the encoders.

Quick start

git clone https://github.com/Luo1Yan/HSA.git
cd HSA
python -m pip install -r requirements.txt huggingface_hub
hf download Rowance/HSA-features --repo-type dataset --local-dir datasets
python run.py --dataset utd-mhad --output outputs/utd-mhad.json

Replace utd-mhad with any dataset below to evaluate it. The UTD-MHAD run reports 9.30% retrieval R@10 and 34.75% classification macro Top-1 for HSA. Expected results for every experiment are in reference_results.json.

What is included

Features for 26 of the 30 main experiments, covering every dataset except Ego4D.

Dataset (--dataset)	Retrieval	Classification	License
TartanRGBT (tartan-rgbt)	6	4	BSD-3-Clause-Clear (LanguageBind), CC BY-NC-SA 4.0 (ImageBind)
VGGSound (vggsound)	2	1	CC BY 4.0 (LanguageBind), CC BY-NC-SA 4.0 (ImageBind)
Caltech Aerial RGBT (caltech-rgbt)	1	0	CC BY-NC-SA 4.0
BatVision (batvision)	2	1	CC BY-SA 4.0
UTD-MHAD (utd-mhad)	1	1	CC BY-NC-SA 4.0
UCF101 (ucf101)	1	0	CC BY-NC 4.0
NYUv2 (nyuv2)	1	1	CC BY-NC 4.0
MSR-VTT (msrvtt)	1	1	CC BY-NC 4.0
MAVD (mavd)	2	0	MAVD license (non-commercial)

Ego4D features are not included because Ego4D is distributed only under its own license. Ego4D licensees can generate them with the code repository.

Each .npz file holds the feature matrices, labels and class prototypes of one experiment; the feature format describes the arrays. LanguageBind video preprocessing includes random horizontal flips, so video features re-extracted from the original data can differ slightly from these files.

License

Each file's license is listed in the table above and in manifest.json. Please also follow the terms of the original datasets (see UPSTREAM_SOURCES.md).

The MAVD files are modified versions of MAVD data (feature vectors computed from MAVD RGB, thermal, audio and scene-description text, assembled on 2026-09-16). They are distributed for non-commercial purposes only under the MAVD license (LICENSES/MAVD_RLLA.txt), including its disclaimer of warranty and limitation of liability.

If you hold rights in one of the source datasets and want a file removed, please open an issue in the code repository.

Citation

@misc{guo2026hsa,
  title={Hub-Spectral Activation of Latent Multimodal Knowledge},
  author={Guo, Ying and Chen, Haidong and Xu, Linrui and Liu, Xiaohao and
          Shi, Chuancheng and Xiao, Canran and Zhang, Dan and Shen, Fei and
          Shen, Li and Chua, Tat-Seng},
  year={2026},
  eprint={2609.17094},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2609.17094}
}

Please also cite the datasets and encoders you use: TartanRGBT (Maheshwari et al., ICRA 2026), VGGSound (Chen et al., ICASSP 2020), Caltech Aerial RGBT (Lee et al., 2024), BatVision (Brunetto et al., IROS 2023), UTD-MHAD (Chen et al., ICIP 2015), UCF101 (Soomro et al., 2012), NYUv2 (Silberman et al., ECCV 2012), MSR-VTT (Xu et al., CVPR 2016), MAVD (Rivera Valverde et al., CVPR 2021), ImageBind (Girdhar et al., CVPR 2023) and LanguageBind (Zhu et al., ICLR 2024).