---
license: cc-by-nc-4.0
task_categories:
  - video-text-to-text
  - visual-question-answering
language:
  - en
tags:
  - video
  - temporal-grounding
  - one-to-many
  - benchmark
  - evaluation
  - multimodal
  - mllm
pretty_name: OMTG Bench
size_categories:
  - n<1K
---

# OMTG Bench: A Benchmark for One-to-Many Temporal Grounding

**OMTG Bench** is the first comprehensive benchmark tailored for the **One-to-Many Temporal Grounding (OMTG)** task, introduced in the paper **"Towards One-to-Many Temporal Grounding"** (ICML 2026, under review).

Unlike conventional temporal grounding benchmarks that assume a one-to-one mapping between a query and a single temporal segment, OMTG Bench evaluates a model's ability to retrieve **all** disjoint temporal segments in a video that correspond to a single textual query — a setting that is ubiquitous in real-world video content but on which state-of-the-art MLLMs (including Gemini-2.5-Pro, Gemini-3-Pro, Seed-1.8, and Qwen2.5-VL / Qwen3-VL families) exhibit a critical capability gap.

## Dataset Summary

| Item | Value |
|---|---|
| Task | One-to-Many Temporal Grounding (OMTG) |
| Release files | `OMTGBench.tsv`, `videos.zip` |
| # Samples | 320 |
| # Unique videos | 287 |
| TSV columns | `id`, `video`, `question`, `answer` |
| Answer format | List of `[start, end]` intervals in seconds |
| Language | English |

### Key Distribution Statistics

The following statistics are computed directly from `OMTGBench.tsv`.

| Property | Value |
|---|---|
| Ground-truth segments per query | 2 - 20 |
| Average segments per query | 3.67 |
| 2–3 segments per query | 62.19% of samples |
| > 6 segments per query | 9.69% of samples |

This distribution makes OMTG Bench a severe test of both **event cardinality perception** and **fine-grained temporal localization**, covering short clips as well as long-form narratives.

## Supported Tasks

- **One-to-Many Temporal Grounding** — given a video `V` and a textual query `Q`, predict the set `{[s₁, e₁], [s₂, e₂], …, [sₖ, eₖ]}` of all disjoint intervals where `Q` occurs.

## Evaluation Metrics

The benchmark introduces a rigorous metric suite that jointly measures precision, recall, and event-count correctness:

| Metric | Description |
|---|---|
| **tIoU** | Average temporal IoU over unions of predicted and ground-truth intervals |
| **C-Acc** | Count Accuracy — the percentage of samples whose predicted cardinality equals the ground-truth cardinality |
| **tF1@ξ** | Temporal F1-Score at IoU threshold `ξ ∈ {0.3, 0.5, 0.7}` under optimal bipartite matching |
| **EtF1** | **Effective Temporal F1** — assigns zero credit to any sample with incorrect predicted cardinality, strictly penalizing under- and over-retrieval and hallucinations |

EtF1 is the recommended primary metric, as it couples instance-level precision–recall with event-count correctness.

## Baseline Results on OMTG Bench

A comprehensive assessment of representative open-source and proprietary MLLMs. All numbers are percentages (%).

| Model | C-Acc | tF1@0.3 | tF1@0.5 | tF1@0.7 | tIoU | EtF1 |
|---|---|---|---|---|---|---|
| **Proprietary Models** | | | | | | |
| Seed-1.8 | 38.12 | 67.13 | 54.67 | 38.79 | 56.81 | 28.04 |
| Gemini-2.5-Pro | 50.94 | 55.72 | 43.57 | 27.97 | 43.24 | 27.80 |
| Gemini-3-Pro | 30.63 | 58.30 | 47.75 | 29.89 | 47.63 | 21.30 |
| **Open-Source General MLLMs** | | | | | | |
| Qwen2.5-VL-3B | 0.00 | 15.17 | 7.01 | 2.86 | 11.60 | 0.00 |
| Qwen2.5-VL-7B | 0.00 | 21.04 | 12.08 | 7.14 | 20.35 | 0.00 |
| Qwen2.5-VL-32B | 0.00 | 16.81 | 9.66 | 4.76 | 18.32 | 0.00 |
| Qwen2.5-VL-72B | 0.00 | 21.16 | 12.20 | 6.88 | 20.02 | 0.00 |
| Qwen3-VL-4B | 0.31 | 37.07 | 26.75 | 17.93 | 30.42 | 0.21 |
| Qwen3-VL-8B | 0.00 | 37.73 | 27.02 | 18.70 | 30.62 | 0.00 |
| Qwen3-VL-30B | 0.00 | 37.03 | 25.98 | 17.52 | 32.36 | 0.00 |
| Qwen3-VL-235B | 0.31 | 34.66 | 25.25 | 16.45 | 25.56 | 0.21 |
| **Temporal-Grounding Experts** | | | | | | |
| VideoChat-R1-7B | 0.00 | 32.07 | 19.70 | 10.42 | 24.93 | 0.00 |
| VideoChat-R1.5-7B | 0.31 | 28.41 | 15.53 | 9.85 | 27.96 | 0.10 |
| Time-R1-7B | 0.00 | 28.94 | 18.73 | 10.00 | 24.11 | 0.00 |
| UniTime | 0.00 | 35.27 | 30.15 | 23.58 | 37.12 | 0.00 |
| Timelens-8B | 0.00 | 39.14 | 32.76 | 22.58 | 32.38 | 0.00 |
| **Ours** | | | | | | |
| **OMTG-4B** | **55.63** | **73.46** | **65.40** | **48.96** | **61.24** | **43.65** |

### Performance Gain of OMTG-4B across Training Stages

| Stage | C-Acc | tF1@0.3 | tF1@0.5 | tF1@0.7 | tIoU | EtF1 |
|---|---|---|---|---|---|---|
| Base (Qwen3-VL-4B) | 0.31 | 37.07 | 26.75 | 17.93 | 30.42 | 0.21 |
| + SFT (on OMTG-56K) | 44.06 | 69.57 | 61.23 | 45.63 | 56.94 | 34.81 |
| + RL (GRPO) | **55.63** | **73.46** | **65.40** | **48.96** | **61.24** | **43.65** |

Key observations:

- Standard open-source models (Qwen2.5-VL series) yield **0% C-Acc / 0% EtF1**, failing to capture the one-to-many complexity.
- Even the newer Qwen3-VL family (4B → 235B) barely exceeds 0% on cardinality-sensitive metrics.
- Advanced proprietary models (Seed-1.8, Gemini-2.5/3-Pro) reach only **21–28% EtF1**.
- Our **OMTG-4B** achieves a new state of the art at **43.65% EtF1**, outperforming the best proprietary baseline (Seed-1.8) by **+15.61 EtF1**.

## Dataset Structure

### Files

```
omtg_bench/
├── OMTGBench.tsv   # 320 annotated samples (72 kB)
└── videos.zip      # corresponding source videos
```

## Usage

```python
from datasets import load_dataset

bench = load_dataset("insomnia7/omtg_bench", split="train")
print(bench[0])
```

Videos can be extracted from `videos.zip` and matched via the `video` column.

### Recommended Evaluation Protocol

1. For each sample, prompt the model with the video and the textual query.
2. Parse the model output into a set of `[start, end]` intervals (seconds).
3. Compute **C-Acc**, **tF1@{0.3, 0.5, 0.7}**, **tIoU**, and **EtF1** via optimal bipartite matching against the ground-truth segments.
4. Report **EtF1** as the primary metric.

## Construction & Quality Control

This repository release contains a tab-separated benchmark file (`OMTGBench.tsv`) and an accompanying video archive (`videos.zip`). Each row in `OMTGBench.tsv` contains:

- `id`: sample identifier.
- `video`: video filename in `videos.zip`.
- `question`: textual query for one-to-many temporal grounding.
- `answer`: the full list of ground-truth `[start, end]` intervals in seconds.

## Intended Use

- Evaluating MLLMs on one-to-many video temporal grounding.
- Studying event cardinality perception and hallucination in video-language models.
- Diagnosing over- and under-retrieval behaviors in long-form video understanding.

## Limitations

- Source videos inherit biases and licenses from the original datasets.
- Domain coverage, while diverse, is not exhaustive; extremely long videos (> 20 min) are underrepresented.
- Manual annotations, although expert-verified, may contain rare residual noise.
- Intended for non-commercial research only.

## Companion Dataset

- Training dataset: [insomnia7/omtg56k](https://huggingface.co/datasets/insomnia7/omtg56k) — 56k high-quality instruction-tuning samples (46k SFT + 10k RL).

## License

Released under **CC BY-NC 4.0** for non-commercial research use. Source videos remain subject to their original dataset licenses.

## Citation

```bibtex
@inproceedings{omtg2026,
  title     = {Towards One-to-Many Temporal Grounding},
  author    = {Anonymous Authors},
  booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
  year      = {2026},
  note      = {Under review}
}
```

## Contact

For questions or issues, please open a discussion on the dataset's Hugging Face page.