Datasets:

Abdrah
/

scout-eccv-pab-annotations

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

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, 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:

@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}
}