Datasets:

Rowance
/

HSA-features

@@ -1,5 +1,5 @@
 ---
-pretty_name: HSA Frozen Feature Archives
 license:
   - cc-by-nc-sa-4.0
   - cc-by-nc-4.0
@@ -14,140 +14,70 @@ tags:
   - 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 **26 of the 30 main-experiment archives** (17 retrieval
-and 9 classification relations, 1,736,396,029 bytes). The four Ego4D archives
-are not included: Ego4D data is available only under the Ego4D license, and
-licensees can generate these archives 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 |
-| BatVision | ImageBind | 1 | 1 | CC BY-SA 4.0 |
-| UTD-MHAD | ImageBind | 1 | 1 | CC BY-NC-SA 4.0 |
-| UCF101 | LanguageBind | 1 | 0 | CC BY-NC 4.0 |
-| NYUv2 | LanguageBind | 1 | 1 | CC BY-NC 4.0 |
-| MSR-VTT | LanguageBind | 1 | 1 | CC BY-NC 4.0 |
-| MAVD | ImageBind | 1 | 0 | MAVD license (non-commercial) |
-| MAVD | LanguageBind | 1 | 0 | MAVD license (non-commercial) |
-| **Total** | | **17** | **9** | |
-The terms of each file are also recorded as `proposed_feature_release_terms` in
-`manifest.json`.
-- The ImageBind license (CC BY-NC-SA 4.0) does not state whether it covers
-  features computed with the model. ImageBind-derived archives are released
-  under CC BY-NC-SA 4.0 except where the source dataset requires its own terms
-  (BatVision: CC BY-SA 4.0; MAVD: MAVD license).
-- UTD-MHAD, UCF101, NYUv2 and MSR-VTT publish no license. Their archives are
-  released for non-commercial research use under the terms in the table; use of
-  the underlying data remains subject to the original datasets' terms.
-- **MAVD notice.** The MAVD-derived archives are modified versions of MAVD data:
-  they contain feature vectors computed from MAVD RGB, thermal and audio data
-  (and scene-description text) by frozen encoders, assembled on 2026-09-16. They
-  are distributed for non-commercial purposes only under the MAVD license
-  ([`LICENSES/MAVD_RLLA.txt`](LICENSES/MAVD_RLLA.txt)), including its
-  disclaimer of warranty and limitation of liability.
-If you hold rights in a source dataset and object to an archive, please open an
-issue in the [code repository](https://github.com/Luo1Yan/HSA/issues) and we
-will remove it. Please cite the original datasets and encoders you use (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 26 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, VGGSound and MAVD 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 huggingface_hub
 hf download Rowance/HSA-features --repo-type dataset --local-dir datasets
-for d in tartan-rgbt vggsound caltech-rgbt batvision utd-mhad ucf101 nyuv2 msrvtt mavd; do
-  python run.py --dataset $d --output outputs/$d.json
-done
 ```
-Select by dataset as above: running `python run.py` without a selection
-expects all 30 archives, including the four Ego4D 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 | 17 | 20.23% | 33.77% |
-| Classification macro Top-1 | 9 | 33.93% | 59.14% |
-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 26 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
@@ -165,16 +95,10 @@ re-extracted from raw videos may differ slightly from these archives.
 }
 ```
-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)).
-- UTD-MHAD: C. Chen, R. Jafari, N. Kehtarnavaz, *UTD-MHAD: A Multimodal Dataset for Human Action Recognition Utilizing a Depth Camera and a Wearable Inertial Sensor*, ICIP 2015.
-- UCF101: K. Soomro, A. R. Zamir, M. Shah, *UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild*, CRCV-TR-12-01, 2012 ([arXiv:1212.0402](https://arxiv.org/abs/1212.0402)).
-- NYUv2: N. Silberman, D. Hoiem, P. Kohli, R. Fergus, *Indoor Segmentation and Support Inference from RGBD Images*, ECCV 2012.
-- MSR-VTT: J. Xu, T. Mei, T. Yao, Y. Rui, *MSR-VTT: A Large Video Description Dataset for Bridging Video and Language*, CVPR 2016.
-- MAVD: F. Rivera Valverde, J. V. Hurtado, A. Valada, *There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge*, CVPR 2021.
-- 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.

 ---
+pretty_name: HSA Frozen Features
 license:
   - cc-by-nc-sa-4.0
   - cc-by-nc-4.0
   - 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
 }
 ```
+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).

UPSTREAM_SOURCES.md CHANGED Viewed

@@ -1,7 +1,6 @@
 # Upstream sources and terms
-Checked on 2026-11-02. Exact acquisition sources and revisions are recorded in
-`protocols/downloads.json`.
 ## Datasets in this repository

 # Upstream sources and terms
+Exact acquisition sources and revisions are recorded in `protocols/downloads.json`.
 ## Datasets in this repository

manifest.json CHANGED Viewed

The diff for this file is too large to render. See raw diff

reference_results.json CHANGED Viewed

@@ -1,1055 +1,1053 @@
-{
-  "evaluation_code_commit": "64b3e9ca01abefb878b02f546f0b593ee459d6cb",
-  "evaluated_date": "2026-10-29",
-  "evaluation_scope": "26 released archives (Ego4D excluded)",
-  "results": [
-    {
-      "task": "retrieval",
-      "backbone": "imagebind",
-      "relation": "text_audio__vggsound",
-      "dataset": "vggsound",
-      "protocol_sha256": "5b3687cc969dd08455cf96f5fb6898fa63d68d5ecab756674db2f58ec0c19df4",
-      "metrics": {
-        "FrozenCosine": {
-          "R@1": 0.652,
-          "R@5": 0.742,
-          "R@10": 0.752,
-          "A_to_B": {
-            "R@1": 0.76,
-            "R@5": 0.94,
-            "R@10": 0.96
-          },
-          "B_to_A": {
-            "R@1": 0.544,
-            "R@5": 0.544,
-            "R@10": 0.544
-          }
-        },
-        "HSA": {
-          "R@1": 0.7995,
-          "R@5": 0.8445,
-          "R@10": 0.8445,
-          "A_to_B": {
-            "R@1": 0.91,
-            "R@5": 1.0,
-            "R@10": 1.0
-          },
-          "B_to_A": {
-            "R@1": 0.689,
-            "R@5": 0.689,
-            "R@10": 0.689
-          }
-        }
-      },
-      "fit": {
-        "carrier_rank": 256,
-        "resolution_rank": 34,
-        "gate": 0.18292140463387738,
-        "carrier_scale": 12.081369970939418,
-        "resolution_scale": 0.052905998829208864
-      }
-    },
-    {
-      "task": "retrieval",
-      "backbone": "imagebind",
-      "relation": "text_thermal__tartan_category",
-      "dataset": "tartan-rgbt",
-      "protocol_sha256": "5cba4b0fd5ed79dfd045a715891fb3b915985cb6ac9f823c1b59dd75be7036a9",
-      "metrics": {
-        "FrozenCosine": {
-          "R@1": 0.020938023450586263,
-          "R@5": 0.06560580681183696,
-          "R@10": 0.07677275265214963,
-          "A_to_B": {
-            "R@1": 0.02233389168062535,
-            "R@5": 0.11166945840312674,
-            "R@10": 0.13400335008375208
-          },
-          "B_to_A": {
-            "R@1": 0.01954215522054718,
-            "R@5": 0.01954215522054718,
-            "R@10": 0.01954215522054718
-          }
-        },
-        "HSA": {
-          "R@1": 0.5946398659966499,
-          "R@5": 0.7375767727526521,
-          "R@10": 0.7487437185929648,
-          "A_to_B": {
-            "R@1": 0.6917922948073701,
-            "R@5": 0.9776661083193746,
-            "R@10": 1.0
-          },
-          "B_to_A": {
-            "R@1": 0.49748743718592964,
-            "R@5": 0.49748743718592964,
-            "R@10": 0.49748743718592964
-          }
-        }
-      },
-      "fit": {
-        "carrier_rank": 256,
-        "resolution_rank": 22,
-        "gate": 0.038638751956733594,
-        "carrier_scale": 7.202986262256294,
-        "resolution_scale": 0.04475672893101064
-      }
-    },
-    {
-      "task": "retrieval",
-      "backbone": "imagebind",
-      "relation": "audio_depth__batvision",
-      "dataset": "batvision",
-      "protocol_sha256": "293a553e31a87b1665447433fa8c0e6973ec4f10f6e6c0170e69428c3a1a4bc0",
-      "metrics": {
-        "FrozenCosine": {
-          "R@1": 0.0017123287671232876,
-          "R@5": 0.007705479452054794,
-          "R@10": 0.019691780821917807,
-          "A_to_B": {
-            "R@1": 0.0017123287671232876,
-            "R@5": 0.005136986301369863,
-            "R@10": 0.02054794520547945
-          },
-          "B_to_A": {
-            "R@1": 0.0017123287671232876,
-            "R@5": 0.010273972602739725,
-            "R@10": 0.018835616438356163
-          }
-        },
-        "HSA": {
-          "R@1": 0.00684931506849315,
-          "R@5": 0.03681506849315068,
-          "R@10": 0.06934931506849315,
-          "A_to_B": {
-            "R@1": 0.005136986301369863,
-            "R@5": 0.032534246575342464,
-            "R@10": 0.06506849315068493
-          },
-          "B_to_A": {
-            "R@1": 0.008561643835616438,
-            "R@5": 0.0410958904109589,
-            "R@10": 0.07363013698630137
-          }
-        }
-      },
-      "fit": {
-        "carrier_rank": 256,
-        "resolution_rank": 16,
-        "gate": 0.08883661431100176,
-        "carrier_scale": 6.293079516075885,
-        "resolution_scale": 0.05852520715466934
-      }
-    },
-    {
-      "task": "retrieval",
-      "backbone": "imagebind",
-      "relation": "audio_thermal__mavd",
-      "dataset": "mavd",
-      "protocol_sha256": "1f006ab1d0fd1f829a78d79ab9a0c6b51af7c7df211ca408a586f8941311a1db",
-      "metrics": {
-        "FrozenCosine": {
-          "R@1": 0.0011574074074074073,
-          "R@5": 0.005787037037037037,
-          "R@10": 0.015046296296296295,
-          "A_to_B": {
-            "R@1": 0.0,
-            "R@5": 0.006944444444444444,
-            "R@10": 0.016203703703703703
-          },
-          "B_to_A": {
-            "R@1": 0.0023148148148148147,
-            "R@5": 0.004629629629629629,
-            "R@10": 0.013888888888888888
-          }
-        },
-        "HSA": {
-          "R@1": 0.006944444444444444,
-          "R@5": 0.02546296296296296,
-          "R@10": 0.046296296296296294,
-          "A_to_B": {
-            "R@1": 0.009259259259259259,
-            "R@5": 0.02546296296296296,
-            "R@10": 0.041666666666666664
-          },
-          "B_to_A": {
-            "R@1": 0.004629629629629629,
-            "R@5": 0.02546296296296296,
-            "R@10": 0.05092592592592592
-          }
-        }
-      },
-      "fit": {
-        "carrier_rank": 256,
-        "resolution_rank": 3,
-        "gate": 0.018656684741055675,
-        "carrier_scale": 19.075512317876477,
-        "resolution_scale": 0.07868237930337214
-      }
-    },
-    {
-      "task": "retrieval",
-      "backbone": "imagebind",
-      "relation": "depth_thermal__tartan",
-      "dataset": "tartan-rgbt",
-      "protocol_sha256": "5c98d1d4d4867f311322aef4543a567264cbeb1b92b2aaad0e55ea1448902c15",
-      "metrics": {
-        "FrozenCosine": {
-          "R@1": 0.0004069294268108359,
-          "R@5": 0.0016858504825020347,
-          "R@10": 0.0033717009650040693,
-          "A_to_B": {
-            "R@1": 0.0003487966515521451,
-            "R@5": 0.0017439832577607255,
-            "R@10": 0.003487966515521451
-          },
-          "B_to_A": {
-            "R@1": 0.0004650622020695268,
-            "R@5": 0.0016277177072433438,
-            "R@10": 0.0032554354144866877
-          }
-        },
-        "HSA": {
-          "R@1": 0.011975351703290314,
-          "R@5": 0.05563306592256714,
-          "R@10": 0.09661667247994418,
-          "A_to_B": {
-            "R@1": 0.011859086152772933,
-            "R@5": 0.056272526450412745,
-            "R@10": 0.09836065573770492
-          },
-          "B_to_A": {
-            "R@1": 0.012091617253807696,
-            "R@5": 0.05499360539472154,
-            "R@10": 0.09487268922218346
-          }
-        }
-      },
-      "fit": {
-        "carrier_rank": 256,
-        "resolution_rank": 18,
-        "gate": 0.15585655823261302,
-        "carrier_scale": 7.034712447652384,
-        "resolution_scale": 0.058730341347603396
-      }
-    },
-    {
-      "task": "retrieval",
-      "backbone": "imagebind",
-      "relation": "depth_imu__utd",
-      "dataset": "utd-mhad",
-      "protocol_sha256": "dd6ff6116349bb70b0fb9eac2deee1ac2f7d02e4580d4b0e17328f446e953f53",
-      "metrics": {
-        "FrozenCosine": {
-          "R@1": 0.004651162790697674,
-          "R@5": 0.01744186046511628,
-          "R@10": 0.030232558139534883,
-          "A_to_B": {
-            "R@1": 0.004651162790697674,
-            "R@5": 0.01627906976744186,
-            "R@10": 0.030232558139534883
-          },
-          "B_to_A": {
-            "R@1": 0.004651162790697674,
-            "R@5": 0.018604651162790697,
-            "R@10": 0.030232558139534883
-          }
-        },
-        "HSA": {
-          "R@1": 0.00813953488372093,
-          "R@5": 0.05116279069767442,
-          "R@10": 0.09302325581395349,
-          "A_to_B": {
-            "R@1": 0.002325581395348837,
-            "R@5": 0.04883720930232558,
-            "R@10": 0.09767441860465116
-          },
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