---
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - streaming-video
  - streaming-evaluation
  - interaction-models
  - streaming-benchmark
  - temporal-localization
pretty_name: SPOT-Bench
viewer: false
extra_gated_prompt: >-
  Before downloading SPOT-Bench, please request access to the source datasets:
  [Ego4D](https://ego4d.dev/request/ego4d) and
  [Ego-Exo4D](https://ego4d.dev/request/ego-exo4d).
---

# SPOT-Bench

A fully **proactive** benchmark for **streaming video models** and **interaction models**.

*⚡ **TL;DR:** SPOT-Bench requires a streaming model to monitor a live video stream and proactively decide when to respond. Every prediction across the full video is evaluated using the Timeliness-F1 metric.*

> [!IMPORTANT]
> **Detection** tasks (ABD, PNR) are released in full. For **Interaction** (SQA, SPG) and
> **Intervention** (SI, UI) we release a representative validation set while holding out the test set for an upcoming challenge.
> Stay tuned for updates on our [webpage](https://dibschat.github.io/SPOT-Bench/).

### Contents

```text
spot-bench/
  abd.json          # Action Boundary Detection
  pnr.json          # Point-of-No-Return Detection
  sqa.json          # Streaming Question Answering
  spg.json          # Streaming Procedural Guidance
  si.json           # Solicited Intervention
  ui.json           # Unsolicited Intervention
  videos.zip        # 662 MP4 videos, ~40 GB
  README.md
```

### Benchmark Statistics

| Category | Task | File | Videos | Turns | Slots |
| --- | --- | --- | ---: | ---: | ---: |
| Detection (full) | ABD | `abd.json` | 322 | 1,621 | 2,340 |
| Detection (full) | PNR | `pnr.json` | 286 | 1,159 | 1,234 |
| Interaction (val) | SQA | `sqa.json` | 15 | 95 | 101 |
| Interaction (val) | SPG | `spg.json` | 13 | 13 | 95 |
| Intervention (val) | SI | `si.json` | 13 | 13 | 40 |
| Intervention (val) | UI | `ui.json` | 13 | 13 | 35 |
| **Total** | | | **662** | **2,914** | **3,845** |

### Notes
- **Detection tasks are closed-vocabulary.** The expected response is specified in the question and is a single token, enabling fast, deterministic evaluation with no LLM-judge required.
- **Video-only.** All audio tracks have been removed. SPOT-Bench focuses exclusively on streaming video understanding and visual proactivity.
- **Original IDs.** Filenames retain the original source-dataset video IDs, allowing each entry to be traced back to its source.

### Evaluation

Evaluation code, baselines, and scoring metrics are provided on [Github](https://github.com/dibschat/SPOT-Bench). Place the unzipped `videos/` directory and the six JSON files under `data/`


### License

SPOT-Bench is released under the CC BY-NC-SA 4.0 license. For video sources, please refer to the original dataset licenses: [Ego4D](https://ego4d.dev/request/ego4d), [Ego-Exo4D](https://ego4d.dev/request/ego-exo4d), [HTStep](https://github.com/facebookresearch/htstep), [HoloAssist](https://holoassist.github.io/), [EgoBlind](https://github.com/doc-doc/EgoBlind), [MovieNet](https://movienet.github.io/), [Perception Test](https://github.com/google-deepmind/perception_test) and [THUMOS14](https://www.crcv.ucf.edu/THUMOS14/home.html).

## Citation

```bibtex
@article{chatterjee2026don,
  title={Don't Pause! Every prediction matters in a streaming video},
  author={Chatterjee, Dibyadip and Pang, Zhanzhong and Sener, Fadime and Song, Yale and Yao, Angela},
  journal={arXiv preprint arXiv:2604.24317},
  year={2026}
}
```