| --- |
| license: cc-by-4.0 |
| pretty_name: CS-CLIP Training Annotations |
| language: |
| - en |
| tags: |
| - image-text-retrieval |
| - compositionality |
| - clip |
| - arxiv:2602.23906 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # CS-CLIP Training Annotations |
|
|
| Prepared training annotations for **[Half-Truths Break Similarity-Based Retrieval](https://arxiv.org/abs/2602.23906)**, NeurIPS 2026. Bora Kargi, Arnas Uselis, Seong Joon Oh. |
|
|
| This release contains **410,340 caption records** from the eight annotation files used by the reported CS-CLIP run. All reference COCO `train2014` images. Caption counts are not unique image counts. Images are downloaded separately from COCO. |
|
|
| The [verified Half-Truth evaluation dataset](https://huggingface.co/datasets/kbora/Half-Truths) is released separately. |
|
|
| ## Train using the original files |
|
|
| The archive preserves the training JSON files exactly, including their original field names. The [CS-CLIP repository](https://github.com/kargibora/CS-CLIP/tree/release/paper-reproduction) provides training code. |
|
|
| ```bash |
| hf download kbora/CS-CLIP-Training original/training-json.tar.gz \ |
| --repo-type dataset --local-dir datasets/CS-CLIP-Training |
| mkdir -p datasets/CS-CLIP-Training/json |
| tar -xzf datasets/CS-CLIP-Training/original/training-json.tar.gz \ |
| -C datasets/CS-CLIP-Training/json |
| ``` |
|
|
| `image_path` is relative to the image root, for example `datasets/COCO/train2014/COCO_train2014_000000057870.jpg`. With this directory layout, set `IMAGE_ROOT=.`. |
|
|
| ## Browse the annotations |
|
|
| ```python |
| from datasets import load_dataset |
| samples = load_dataset("kbora/CS-CLIP-Training", split="train") |
| ``` |
|
|
| The Parquet view exposes the main training fields in a tabular schema: |
|
|
| - `sample_id`, `original_caption`, `image_path`: caption and image references. |
| - `entities`: extracted positive units, called `positive_components` in the original JSON. |
| - `entity_foils`: rows of `positive`, `negative`, and `change_type`, flattened from `negative_components`. |
| - `relations_json`: JSON-encoded relation units and their matched foils, preserving nested source fields. |
| - `swap_negatives`: full-caption shuffled negatives. |
|
|
| Use the original archive for exact training inputs; the Parquet files are a browsing view. `manifest.json` records each original file's SHA-256 and sample count. |
|
|
| ## Construction and limitations |
|
|
| The annotations contain automatically generated entity/relation units, matched foils, and shuffled caption negatives. They are **not human-verified training labels**. An image-grounded VLM audit of 1,000 training foils estimated a 24.6% false-negative rate: some nominally incorrect foils are true for the image. The paper's evaluation uses a separate human-verified suite. The training archive is distributed as used, including that noise. |
|
|
| ## Source assets and licensing |
|
|
| The authors’ generated annotations are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This grant covers their contributions, not third-party source captions or images. |
|
|
| Source captions and images come from [COCO](https://cocodataset.org/). COCO images are not included in this repository and retain their original rights and terms. An annotation license does not relicense source assets. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{kargi2026halftruths, |
| title={Half-Truths Break Similarity-Based Retrieval}, |
| author={Kargi, Bora and Uselis, Arnas and Oh, Seong Joon}, |
| booktitle={Advances in Neural Information Processing Systems}, |
| year={2026}, |
| url={https://arxiv.org/abs/2602.23906} |
| } |
| ``` |
|
|
