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LICENSES/ImageBind_CC-BY-NC-SA-4.0.txt

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LICENSES/LanguageBind_MIT.txt

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+MIT License
+
+Copyright (c) 2023 PKU-YUAN's Group (袁粒课题组-北大信工)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

LICENSES/VGGSound_license.txt

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+OXFORD VGG-Sound Dataset
+
+The dataset is covered under a Creative Commons Attribution 4.0 International license. 
+Please read the license terms here: https://creativecommons.org/licenses/by/4.0/
+
+Downloading this dataset implies agreement to follow the same
+conditions for any modification and/or re-distribution of the dataset in any form.
+
+Additionally any entity using this dataset agrees to the following conditions:
+
+THIS DATASET IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
+IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Please cite [1] below if you make use of the dataset.
+
+[1] H. Chen, W. Xie, A. Vedaldi, A. Zisserman
+VGG-SOUND: A LARGE-SCALE AUDIO-VISUAL DATASET  
+ICASSP, 2020

README.md

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+---
+pretty_name: HSA Frozen Feature Archives
+license:
+  - cc-by-nc-sa-4.0
+  - cc-by-sa-4.0
+  - cc-by-4.0
+  - bsd-3-clause-clear
+task_categories:
+  - feature-extraction
+tags:
+  - multimodal
+  - cross-modal-retrieval
+  - imagebind
+  - languagebind
+  - arxiv:2609.17094
+viewer: false
+---
+
+# HSA Frozen Feature Archives
+
+[Paper](https://arxiv.org/abs/2609.17094) ·
+[Code](https://github.com/Luo1Yan/HSA) ·
+[Upstream sources](UPSTREAM_SOURCES.md)
+
+Frozen ImageBind and LanguageBind features for the main retrieval and
+prototype-classification experiments of
+[*Hub-Spectral Activation of Latent Multimodal Knowledge*](https://arxiv.org/abs/2609.17094).
+With these archives, the HSA and frozen-cosine results can be recomputed with
+the public code without downloading the original media or encoder weights.
+
+This repository contains **15 of the 30 main-experiment archives** (10 retrieval
+and 5 classification relations, 682,601,293 bytes). Each archive is listed below
+with the terms of its source dataset and encoder. The other 15 archives
+(UTD-MHAD, UCF101, NYUv2, MSR-VTT, MAVD, Ego4D, and the two ImageBind BatVision
+archives) are not included; they can be generated from the original data with
+`download.py` and `extract.py` in the code repository.
+
+## Contents
+
+| Source dataset | Encoder | Retrieval | Classification | Archive terms |
+|---|---|---:|---:|---|
+| TartanRGBT | LanguageBind | 4 | 3 | BSD-3-Clause-Clear |
+| TartanRGBT | ImageBind | 2 | 1 | CC BY-NC-SA 4.0 |
+| VGGSound | LanguageBind | 1 | 0 | CC BY 4.0 |
+| VGGSound / VGGSound-309 | ImageBind | 1 | 1 | CC BY-NC-SA 4.0 |
+| Caltech Aerial RGBT | ImageBind | 1 | 0 | CC BY-NC-SA 4.0 |
+| BatVision | LanguageBind | 1 | 0 | CC BY-SA 4.0 |
+| **Total** | | **10** | **5** | |
+
+The terms of each file are also recorded as `proposed_feature_release_terms` in
+`manifest.json`. ImageBind-derived archives follow the ImageBind license
+(CC BY-NC-SA 4.0, non-commercial). Please also respect the source dataset terms
+and cite the original datasets and encoders (see [Citation](#citation)).
+
+## Files
+
+```text
+retrieval/{imagebind,languagebind}/<relation>.npz
+classification/{imagebind,languagebind}/<relation>.npz
+manifest.json            # shapes, sizes, SHA-256, dataset, protocol hash and terms per archive
+SHA256SUMS               # checksums of the 15 archives
+reference_results.json   # per-relation HSA / frozen-cosine results on these archives
+protocols/               # sample IDs, splits, prompts and sources at the evaluation commit
+UPSTREAM_SOURCES.md      # dataset and encoder sources and terms
+LICENSES/                # ImageBind, LanguageBind and VGGSound license texts
+```
+
+## Array format
+
+Load with `numpy.load(path, allow_pickle=False)`. `N` is the training count,
+`M` the test count, `D` the target feature dimension, `H` the hub dimension and
+`C` the number of classes.
+
+| Array | Shape | Description |
+|---|---|---|
+| `a_train`, `h_a_train` | `(N, D)`, `(N, H)` | Modality A and hub features, paired row by row |
+| `b_train`, `h_b_train` | `(N, D)`, `(N, H)` | Modality B and hub features, paired row by row |
+| `a_test`, `b_test` | `(M, D)` each | Retrieval queries and candidates |
+| `positive_labels` | `(M,)` | Optional; a candidate is positive when its label matches the query label, otherwise when its row index matches |
+| `prototypes` | `(C, D)` | Classification prototype bank in modality A |
+| `queries` | `(M, D)` | Classification queries in modality B |
+| `labels` | `(M,)` | Integer prototype index of each query |
+
+The registered protocols use the same training samples for both hub edges, so
+`h_a_train` and `h_b_train` contain the same observations. Sample IDs, split
+order, prompts and preprocessing are fixed in `protocols/` and in the code.
+
+## Usage
+
+```bash
+git clone https://github.com/Luo1Yan/HSA.git
+cd HSA
+git checkout 64b3e9ca01abefb878b02f546f0b593ee459d6cb
+python -m pip install -r requirements.txt
+hf download Rowance/HSA-features --repo-type dataset --local-dir datasets
+python run.py --dataset tartan-rgbt --output outputs/tartan.json
+python run.py --dataset vggsound --output outputs/vggsound.json
+python run.py --dataset caltech-rgbt --output outputs/caltech.json
+python run.py --dataset batvision --backbone languagebind --output outputs/batvision.json
+```
+
+Select by dataset (and backbone for BatVision): running `python run.py` without
+a selection expects all 30 archives. Add `--device cuda` to score retrieval on a
+GPU.
+
+Equal-weight means over the relations in this repository:
+
+| Task | Relations | Frozen cosine | HSA |
+|---|---:|---:|---:|
+| Bidirectional retrieval R@10 | 10 | 22.02% | 40.93% |
+| Classification macro Top-1 | 5 | 42.20% | 69.38% |
+
+The paper's means over all 30 relations are 18.27% / 31.15% (retrieval R@10,
+frozen cosine / HSA) and 29.01% / 52.43% (classification macro Top-1).
+Per-relation values are in `reference_results.json`.
+
+## Provenance
+
+The archives were extracted with frozen ImageBind-Huge (source commit
+`53680b02`) and LanguageBind (source commit `7070c533`; checkpoint revisions in
+`protocols/downloads.json`). All file sizes and SHA-256 hashes match
+`manifest.json`, and all 15 archives pass `run.py --check-data` at commit
+`64b3e9c`. End-to-end re-extraction from raw data has been verified on
+UTD-MHAD. LanguageBind video preprocessing applies random horizontal flips; the
+flip state of the archived video features was not recorded, so features
+re-extracted from raw videos may differ slightly from these archives.
+
+## 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 source datasets and encoders you use:
+
+- TartanRGBT: P. Maheshwari et al., *AnyThermal: Towards Learning Universal Representations for Thermal Perception*, ICRA 2026 ([arXiv:2602.06203](https://arxiv.org/abs/2602.06203)).
+- VGGSound: H. Chen, W. Xie, A. Vedaldi, A. Zisserman, *VGGSound: A Large-Scale Audio-Visual Dataset*, ICASSP 2020.
+- Caltech Aerial RGBT: C. Lee et al., *Caltech Aerial RGB-Thermal Dataset in the Wild*, CaltechDATA ([doi:10.22002/cks6g-ps927](https://doi.org/10.22002/cks6g-ps927)).
+- BatVision: A. Brunetto, S. Hornauer, S. X. Yu, F. Moutarde, *The Audio-Visual BatVision Dataset for Research on Sight and Sound*, IROS 2023 ([data doi:10.57745/HYLZNL](https://doi.org/10.57745/HYLZNL)).
+- ImageBind: R. Girdhar et al., *ImageBind: One Embedding Space To Bind Them All*, CVPR 2023.
+- LanguageBind: B. Zhu et al., *LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment*, ICLR 2024.

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UPSTREAM_SOURCES.md

@@ -0,0 +1,35 @@
+# Upstream sources and terms
+
+Checked on 2026-10-29. Exact acquisition sources and revisions are recorded in
+`protocols/downloads.json`.
+
+## Datasets in this repository
+
+| Dataset | Source | Terms |
+|---|---|---|
+| TartanRGBT | [Official repository at the pinned revision](https://huggingface.co/datasets/theairlabcmu/TartanRGBT/tree/cafaed13bd4e55d3765cdeb0b1357c96d6ff2b79); [project page](https://anythermal.github.io/) | BSD-3-Clause-Clear (dataset card) |
+| VGGSound / VGGSound-309 | [Oxford VGG](https://www.robots.ox.ac.uk/~vgg/data/vggsound/); [license text](https://thor.robots.ox.ac.uk/vggsound/license_vggsound.txt) (copy in `LICENSES/`) | CC BY 4.0; the copyright of the videos remains with their original owners |
+| Caltech Aerial RGBT | [Dataset record](https://data.caltech.edu/records/cks6g-ps927); [DOI 10.22002/cks6g-ps927](https://doi.org/10.22002/cks6g-ps927) | CC BY-NC 4.0 |
+| BatVision | [Dataset DOI 10.57745/HYLZNL](https://doi.org/10.57745/HYLZNL) | CC BY-SA 4.0 |
+
+## Encoders
+
+| Encoder | Source | Terms |
+|---|---|---|
+| ImageBind-Huge | [Source at commit 53680b02](https://github.com/facebookresearch/ImageBind/tree/53680b02d7e37b19b124fa37bae4b6c98c38f5be); [LICENSE](https://github.com/facebookresearch/ImageBind/blob/53680b02d7e37b19b124fa37bae4b6c98c38f5be/LICENSE) (copy in `LICENSES/`) | CC BY-NC-SA 4.0 |
+| LanguageBind | [Source at commit 7070c533](https://github.com/PKU-YuanGroup/LanguageBind/tree/7070c53375661cdb235801176b564b45f96f0648); checkpoint revisions in `protocols/downloads.json` | MIT (copy in `LICENSES/`) |
+
+## Terms applied to each archive
+
+| Archives | Terms |
+|---|---|
+| TartanRGBT × LanguageBind (7) | BSD-3-Clause-Clear |
+| TartanRGBT × ImageBind (3) | CC BY-NC-SA 4.0 |
+| VGGSound × LanguageBind (1) | CC BY 4.0 |
+| VGGSound × ImageBind (2) | CC BY-NC-SA 4.0 |
+| Caltech Aerial RGBT × ImageBind (1) | CC BY-NC-SA 4.0 |
+| BatVision × LanguageBind (1) | CC BY-SA 4.0 |
+
+Each archive contains feature vectors computed by a frozen encoder from the
+listed source data, together with labels and class prototypes. The vectors are
+derived from, and are not copies of, the original media.

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+      "access_review": "source_terms_reviewed_2026-10-29",
+      "release_eligibility": "eligible_with_conditions",
+      "proposed_feature_release_terms": "BSD-3-Clause-Clear",
+      "original_distribution_status": "pending_source_license_review",
+      "distribution_status": "eligible_for_initial_release_with_conditions"
+    },
+    {
+      "path": "classification/languagebind/thermal-depth.npz",
+      "task": "classification",
+      "backbone": "languagebind",
+      "relation": "thermal-depth",
+      "dataset": "TartanRGBT",
+      "bytes": 8287383,
+      "sha256": "0a1f694b27fdbf11c8570acbcd435eeb089ae071a713708071278d7844546ab0",
+      "arrays": {
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+          "dtype": "float32"
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+        "h_a_train": {
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+        "b_train": {
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+        "h_b_train": {
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+          "dtype": "float32"
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+        "prototypes": {
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+        "labels": {
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+        }
+      },
+      "class_names": [
+        "indoor",
+        "offroad",
+        "outdoor",
+        "urban"
+      ],
+      "prototype_description": "mean normalized training-A feature per class",
+      "registered_dataset": "tartan-rgbt",
+      "collection": "tartan-clips",
+      "train_count": 1184,
+      "test_count": 436,
+      "registered_protocol_sha256": "7398e60dc229f8cace02cc699ec5950199dea55c95671a87b2977852375fbd4d",
+      "access_review": "source_terms_reviewed_2026-10-29",
+      "release_eligibility": "eligible_with_conditions",
+      "proposed_feature_release_terms": "BSD-3-Clause-Clear",
+      "original_distribution_status": "pending_source_license_review",
+      "distribution_status": "eligible_for_initial_release_with_conditions"
+    }
+  ],
+  "decision_basis_file": "publication_scope.json"
+}

protocols/caltech-rgbt.json

@@ -0,0 +1 @@
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protocols/downloads.json

@@ -0,0 +1,642 @@
+{
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protocols/text_templates.json

@@ -0,0 +1,166 @@
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reference_results.json

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