| --- |
| pretty_name: HSA Frozen Features |
| license: |
| - cc-by-nc-sa-4.0 |
| - cc-by-nc-4.0 |
| - cc-by-sa-4.0 |
| - cc-by-4.0 |
| - bsd-3-clause-clear |
| - other |
| task_categories: |
| - feature-extraction |
| tags: |
| - multimodal |
| - cross-modal-retrieval |
| - imagebind |
| - languagebind |
| viewer: false |
| --- |
| |
| # HSA Frozen Features |
|
|
| [Paper](https://arxiv.org/abs/2609.17094) · [Code](https://github.com/Luo1Yan/HSA) |
|
|
| Precomputed ImageBind and LanguageBind features for the main experiments of |
| [*Hub-Spectral Activation of Latent Multimodal Knowledge*](https://arxiv.org/abs/2609.17094). |
| With them you can reproduce the paper's HSA and frozen-cosine results without |
| downloading the original datasets or the encoders. |
|
|
| ## Quick start |
|
|
| ```bash |
| 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](https://github.com/Luo1Yan/HSA/blob/main/docs/feature-format.md) |
| 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](https://github.com/Luo1Yan/HSA/issues). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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). |
|
|
