| --- |
| license: mit |
| task_categories: |
| - text-to-image |
| - image-to-text |
| tags: |
| - person-retrieval |
| - text-based-person-search |
| - aicity-challenge |
| pretty_name: PAB hard-negative-pair annotations for SCOUT |
| --- |
| |
| # PAB hard-negative-pair annotations |
|
|
| Supporting data for **SCOUT** (Sim-to-Real Text-Based Person Retrieval by Embedding-Space Prediction |
| over Frozen Video Features), an ECCV 2026 workshop paper on AI City Challenge Track 4 (Text-Based |
| Person Re-Identification, Sim2Real). Code: https://github.com/abtraore/SCOUT-ECCV |
|
|
| ## What this is |
|
|
| The AI City Challenge Track 4 release of PAB (Pedestrian Anomaly Behavior) strips several fields |
| from the dataset authors' original CMP annotation format. This is a re-fetch of the **original, |
| richer** per-image annotation files, joined back in by `data/pab.py`'s hard-negative-pair machinery |
| (`data.use_hard_negs` / `data.hard_neg_annotation_dir` in the training configs, and |
| `--hard-neg-annotation-dir` in `scripts/local_eval.py` for the `val_hard`/`train_hard` splits). |
|
|
| Each `train/attr_N.json` is JSONL (one record per line, despite the `.json` extension), one record |
| per training image, with fields including: |
|
|
| - `image`, `image_id`: the PAB image path and id |
| - `caption`: the training caption for that image |
| - `hard_i`, `hard_i_id`: the CMP-identity-mined hard-negative **image** partner and its id |
| - `hard_c`: the hard-negative partner's caption |
| - `source_id`, `source_caption`: provenance back to the synthetic source |
|
|
| `test/attr.json` and `test/ucc.json`, `multi-weather/*.json`, and `source_caption.json` are the |
| corresponding files for the test/multi-weather splits and the shared caption-source index. |
|
|
| This is **only the annotation metadata**, not image pixels. The PAB imagery itself is obtained |
| separately (see the main repo's README, "Data setup") from the AI City Challenge Track 4 organizers |
| or the dataset authors' own release; it is not redistributed here. |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| annotation_dir = snapshot_download(repo_id="Abdrah/scout-eccv-pab-annotations", repo_type="dataset") |
| ``` |
|
|
| Then point a SCOUT training config or `scripts/local_eval.py --hard-neg-annotation-dir` at |
| `{annotation_dir}/train` (or `/test`). |
|
|
| ## License and provenance |
|
|
| The annotation content originates from the PAB dataset authors' CMP release |
| ([Shuyu-XJTU/CMP](https://github.com/Shuyu-XJTU/CMP), MIT License, Copyright (c) 2025 Shuyu-XJTU) |
| and is redistributed here under the same MIT License, unmodified except for restoring the fields |
| the Track 4 release had stripped. All credit for the dataset and its annotations goes to the PAB |
| authors. |
|
|
| ## Citation |
|
|
| If you use these annotations, cite the PAB/CMP paper: |
|
|
| ```bibtex |
| @inproceedings{yang2025beyondwalking, |
| title = {Beyond walking: A large-scale image-text benchmark for text-based person anomaly search}, |
| author = {Yang, Shuyu and Wang, Yaxiong and Zhu, Li and Zheng, Zhedong}, |
| booktitle = {ICCV}, |
| year = {2025}, |
| note = {arXiv:2411.17776} |
| } |
| ``` |
|
|
