Openevents V1 Eval

---
name: openevents-v1-eval
description: Probes a model's ability to perform multimodal event grounding by integrating visual content with textual context. It evaluates three interdependent capabilities: generating event-aware image captions, retrieving relevant news articles from images, and retrieving images from narrative event descriptions. Use when the user wants to benchmark on OpenEvents V1, or asks about evaluating this task. Reports CLIPScore, mAP.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2506.18372
  bibtex_key: [REDACTED]
  confidence: high
---

# openevents-v1-eval

> OpenEvents V1: Large-Scale Benchmark Dataset for Multimodal Event Grounding — Nguyen et al. (2025) (arXiv:2506.18372, 2025)

## What this evaluates

Probes a model's ability to perform multimodal event grounding by integrating visual content with textual context. It evaluates three interdependent capabilities: generating event-aware image captions, retrieving relevant news articles from images, and retrieving images from narrative event descriptions.

## Datasets

- **OpenEvents V1** — total ?; splits: test (-1)

## Metrics

- `CLIPScore` **(primary)** — range: [0, 1]
  - Measures semantic alignment between image and text embeddings using a pre-trained CLIP model. Higher scores indicate better cross-modal consistency.
- `CIDEr` — range: [0, 1]
  - Consensus-based Image Description Evaluation; computes n-gram TF-IDF weighted overlap between generated and reference captions.
- `BLEU-4` — range: [0, 1]
  - Bilingual Evaluation Understudy; calculates precision of 4-gram matches between prediction and references with brevity penalty.
- `METEOR` — range: [0, 1]
  - Evaluates translation quality using precision, recall, and penalties for fragmentation and synonymy.
- `mAP` **(primary)** — range: [0, 1]
  - Mean Average Precision; averages the precision at each recall threshold across all queries.
- `NDCG` — range: [0, 1]
  - Normalized Discounted Cumulative Gain; measures ranking quality by discounting gains logarithmically based on position.
- `NN` — range: [0, 1]
  - Nearest Neighbor accuracy; fraction of queries where the top-ranked retrieved item matches the ground truth.
- `AUC` — range: [0, 1]
  - Area Under the Precision-Recall Curve; summarizes the trade-off between precision and recall across thresholds.

## Input / output format

**Input**: Task 1: Image (optionally paired with corresponding news article text). Task 2: Image. Task 3: Narrative caption describing a real-world event.

**Output**: Task 1: Text caption. Task 2: Ranked list of news articles. Task 3: Ranked list of images.

## Scoring recipe

```python
# Captioning (Task 1)
clip_score = cosine_sim(CLIP.encode(img), CLIP.encode(pred))
cider, bleu4, meteor = standard_metrics(refs, pred)

# Retrieval (Tasks 2 & 3)
scores = model_similarity(query, candidates)
ranked = argsort(scores, desc=True)
ap = average_precision(ranked, labels)
ndcg = normalized_dcg(ranked, labels)
nn_acc = 1.0 if ranked[0] == labels.argmax() else 0.0
auc = area_under_pr_curve(scores, labels)
```

## Common pitfalls

- Models trained only on visual content (Image-only pipeline) significantly underperform because ground-truth captions contain event-specific details (named entities, timelines, causes) not visible in the image.
- Direct query-to-image retrieval struggles with complex, narrative-style captions that reflect deeper event semantics; hybrid approaches using intermediate article retrieval and re-ranking are necessary for competitive performance.
- Using OpenCLIP instead of CLIP yields lower retrieval accuracy on this dataset due to differences in training data curation (public datasets vs. carefully curated image-text correspondence).

## Evidence (verbatim from paper)

> To assess caption quality, we report standard captioning metrics including CLIPScore, CIDEr, BLEU-4 and METEOR, which measure fluency, descriptiveness, and semantic alignment with reference captions.

## Citation

```bibtex
@misc{nguyen2025openevents,
  title={OpenEvents V1: Large-Scale Benchmark Dataset for Multimodal Event Grounding},
  author={Nguyen et al. (2025)},
  year={2025},
  note={arXiv:2506.18372}
}
```

- arXiv: 2506.18372