---
pretty_name: Déjà Cue Fixed-Feature Evaluation Assets
language:
  - en
license: other
license_name: deja-cue-data-and-feature-terms
license_link: https://github.com/HaofanCao/DejaCue/blob/main/DATA_LICENSE.md
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
multilinguality:
  - monolingual
source_datasets:
  - extended
size_categories:
  - n<1K
tags:
  - video
  - computer-vision
  - temporal-localization
  - moment-retrieval
  - object-tracking
  - reproducibility
---

<div align="center">

<h1 align="center">Déjà Cue Fixed-Feature Evaluation Assets</h1>

<p><strong>Frozen features, fixed evaluation settings, exact reference windows, and statistical results for Déjà Cue.</strong></p>

<p align="center">Haofan Cao · Zhichao You · Yunkai Yang · Liang Guo · Jie Wang · Chongshou Li</p>

<p align="center">
  <a href="https://arxiv.org/abs/2608.02044"><img src="https://img.shields.io/badge/arXiv-2608.02044-b31b1b.svg" alt="arXiv"></a>
  <a href="https://github.com/HaofanCao/DejaCue"><img src="https://img.shields.io/badge/Companion%20Code-GitHub-181717.svg?logo=github&logoColor=white" alt="Companion GitHub code"></a>
  <a href="DATA_LICENSE.md"><img src="https://img.shields.io/badge/Data%20Terms-Read%20before%20use-7C3AED.svg" alt="Data terms"></a>
</p>
</div>

<p align="center">
  <img src="assets/method_overview.png" alt="Overview of the Déjà Cue retrieval framework" width="100%">
</p>

## 📄 Abstract

Tracking links observations of the same object through visual change, yet cannot by itself determine when the object is empty or filled, intact or cut. Déjà Cue formulates identity-conditioned state-moment retrieval: given a tracked-object history and alternative state descriptions, it localizes an interval in which each described state holds. The method turns the alternatives into a vocabulary-relative coordinate system, subtracts their state-balanced centroid from each description, calibrates frame scores, and scans multiple durations within contiguous visible runs using a frozen encoder. On 78 VOST histories, changing only the query reference nearly doubles R@1 at tIoU 0.5 from 10.3% to 20.5% and raises Top-1 tIoU from 16.0% to 21.5%.

## 🔥 News

- **2026.08.05** · The official code and fixed-feature evaluation assets are now open source.

## 📦 Dataset Summary

This repository contains the same `data/` directory as the companion code release. It reproduces retrieval, interval selection, metrics, and statistical calculations without redistributing native video or rerunning the frozen encoder.

Dataset page: [HaofanCao/deja-cue-data](https://huggingface.co/datasets/HaofanCao/deja-cue-data)

| Group | Contents |
| --- | --- |
| VOST evaluation | 78 histories, 156 states, 312 descriptions, reference intervals, and frozen features |
| Prompt variants | Raw, photo, definite, and normalized three-form text features |
| Development set | 5 histories, 13 states, 26 descriptions, 46 positive episodes, and 86 training records |
| Seven-history evaluation | 7 histories, 16 states, 32 descriptions, 59 reference episodes, and 8 auxiliary tracks |
| Reference results | Exact selected windows and the rows used for the reported statistical calculations |
| Evaluation settings | Fixed VOST split lists, final cohort information, method settings, and checksums |

The dataset contains 288 files under `data/`. `DATA_MANIFEST.json` records the byte size and SHA-256 digest of every one.

The Hub's `n<1K` label describes the number of dataset examples, not storage size. The complete repository occupies about 51.5 MB.

## ⬇️ Download and Verify

The complete dataset is published at `HaofanCao/deja-cue-data`. With the current Hugging Face CLI, download it with:

```bash
export DEJA_CUE_DATASET_ID=HaofanCao/deja-cue-data
hf download "$DEJA_CUE_DATASET_ID" --type dataset --local-dir deja-cue-data
cd deja-cue-data
python verify_data.py
```

The script checks every file name, byte size, and SHA-256 value. A Python download is equivalent:

```python
from pathlib import Path

from huggingface_hub import snapshot_download

dataset_root = Path(
    snapshot_download(
        repo_id="HaofanCao/deja-cue-data",
        repo_type="dataset",
        local_dir="deja-cue-data",
    )
)
```

## 🔎 Load One History

```python
import numpy as np

visual_path = dataset_root / "data/features/siglip2/H001/visual_features.npz"
with np.load(visual_path, allow_pickle=False) as arrays:
    frame_indices = arrays["frame_indices"]
    visual_features = arrays["visual_features"]
    visibility_count = arrays["visibility_count"]

print(frame_indices.shape, visual_features.shape, visibility_count.shape)
```

Primary visual archives contain:

| Field | Meaning |
| --- | --- |
| `frame_indices` | Original temporal indices for observed target frames |
| `visual_features` | Unit-normalized 768-dimensional SigLIP 2 features |
| `visibility_count` | Number of target-lineage masks contributing at each frame |

Primary text archives contain `state_ids`, `state_texts`, and unit-normalized `text_features`. Prompt-variant archives additionally contain `variant_names`. JSON manifests define sibling-state grouping, inclusive reference intervals, source-component aggregation, and relative feature paths.

## 🔗 Use with the Code Release

The companion GitHub repository already includes the same `data/` files, so a complete clone can reproduce the CPU results immediately:

```bash
git clone https://github.com/HaofanCao/DejaCue.git
cd DejaCue
python -m pip install -e ".[test]"
python scripts/reproduce_main.py --device cpu
```

When using a separate Hub download, place or link its `data/` directory at the code repository root without changing the relative paths. The code repository supplies the data loaders, retrieval implementation, reproduction commands, annotation and feature tools, and tests.

## 🗂️ Repository Layout

```text
data/
  benchmark.json              Primary benchmark manifest
  features/                   VOST visual, text, and prompt features
  learned/development/        Frozen learned-decoder development inputs
  seven_history/              Seven-history features and auxiliary tracks
  reference/                  Exact windows and statistical results
  protocol/vost/              Fixed VOST split lists and protocol notes
DATA_MANIFEST.json            File sizes and SHA-256 checksums
DATA_LICENSE.md               Data and derived-feature terms
verify_data.py                File verification script
```

Evaluation identifiers such as `H001` are stable IDs used to align features, states, descriptions, and references. All manifest paths are relative to the repository root.

## 🌐 Data Sources

| Asset family | Upstream research source | What this repository contains |
| --- | --- | --- |
| Primary histories | [VOST](https://openaccess.thecvf.com/content/CVPR2023/html/Tokmakov_Breaking_the_Object_in_Video_Object_Segmentation_CVPR_2023_paper.html) | Derived object-local features, evaluation IDs, fixed settings, and reference intervals |
| Six seven-history sequences | [HyperNeRF](https://doi.org/10.1145/3478513.3480487) | Derived target and auxiliary-track features plus evaluation metadata |
| Coffee-martini sequence | [Neural 3D Video](https://doi.org/10.1109/CVPR52688.2022.00544) | Derived target features and evaluation metadata |
| Recurrence diagnostic | [D-NeRF](https://doi.org/10.1109/CVPR46437.2021.01018) | Predictions and reference intervals, not rendered RGB |
| Feature encoder | [SigLIP 2](https://arxiv.org/abs/2502.14786) | Unit-normalized derived embeddings; no model weights |

Feature extraction uses `google/siglip2-base-patch16-224` at revision `75de2d55ec2d0b4efc50b3e9ad70dba96a7b2fa2`. Upstream media and model terms continue to apply to derived assets; see [DATA_LICENSE.md](DATA_LICENSE.md).

## 📝 Annotation and Included Files

For VOST, two state descriptions per state are fixed before labeling. Two annotators independently label target-lineage frames as pre-state, transition, post-state, or unobserved while blind to method scores, and every disagreement is resolved to consensus. The 100-history selection retains 78 qualifying histories and excludes 22 with no qualifying event.

The dataset includes the split lists, final cohort information, annotation validation scripts, and agreement and annotation-sensitivity summaries. It does not redistribute native RGB, target-lineage masks, pretrained encoder weights, generated decoder checkpoints, the two complete annotation files, or the resolved framewise consensus.

## ✅ Intended Uses

- Reproduction of the primary and extended VOST evaluations.
- Reproduction of the seven-history coordinate study.
- Training the documented eight learned decoders on the five-history development set and evaluating generated checkpoints.
- Recalculation of the reported robustness, diagnostic, and statistical summaries.
- Comparison of alternative retrieval methods under the same frozen representation and evaluation protocol.

## ⚠️ Limitations

The task assumes target-lineage masks, a closed sibling vocabulary, and one selected interval per description. It has no calibrated null output for an absent state. VOST uses binary event-adjacent states and scores one designated occurrence; the dataset also contains 13 additional pre-state and 20 additional post-state occurrences. The seven-history set adds multi-state, interior, recurrent, and progressive cases, but remains small and its three independently held-out histories show mixed coordinate effects.

Fixed features isolate retrieval logic from encoder execution and do not establish behavior for other encoders. Tracking stresses use controlled feature mixing and identity switches rather than pixel-level mask drift. The release does not establish performance for automatically discovered identities, open vocabularies, broad multi-state collections, every natural tracking failure, or hardware-specific latency.

## 🛡️ Privacy and Responsible Use

No native RGB, video, masks, direct identifiers, or model weights are included. The embeddings remain derived from external research media and must be handled under the corresponding source terms. These assets are intended for scientific retrieval and reproducibility research, not biometric identification, surveillance, or inferring sensitive attributes.

## ⚖️ Licensing

This repository combines metadata created for Déjà Cue with embeddings derived from external research data, so its Hugging Face license field is `other`. Read [DATA_LICENSE.md](DATA_LICENSE.md) before reuse or redistribution, and retain the upstream attributions required for the source assets.

## 📚 Citation

If you use these assets, please cite the official paper record:

```bibtex
@misc{cao2026dejacue,
  title         = {Déjà Cue: Localizing States in Object Histories via Vocabulary-Relative Coordinates},
  author        = {Haofan Cao and Zhichao You and Yunkai Yang and Liang Guo and Jie Wang and Chongshou Li},
  year          = {2026},
  eprint        = {2608.02044},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2608.02044}
}
```

**Paper:** [**arXiv:2608.02044**](https://arxiv.org/abs/2608.02044)