Datasets:
Size:
100K<n<1M
ArXiv:
Tags:
egocentric-video
mistake-detection
temporal-localization
video-language-grounding
hand-object-interaction
action-recognition
License:
File size: 2,736 Bytes
4e57a65 e2b50a5 4e57a65 e2b50a5 4e57a65 e2b50a5 4e57a65 e2b50a5 4e57a65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | ---
license: cc-by-4.0
task_categories:
- video-classification
- video-text-to-text
- object-detection
tags:
- egocentric-video
- mistake-detection
- temporal-localization
- video-language-grounding
- hand-object-interaction
- action-recognition
- procedural-activities
- semantic-role-labeling
- ego4d
- epic-kitchens
- point-of-no-return
- cvpr2026
pretty_name: MATT-Bench
size_categories:
- 100K<n<1M
---
# Mistake Attribution: Fine-Grained Mistake Understanding in Egocentric Videos
**CVPR 2026**
[Yayuan Li](https://www.linkedin.com/in/yayuan-li-148659272/)<sup>1</sup>, [Aadit Jain](https://www.linkedin.com/in/jain-aadit/)<sup>1</sup>, [Filippos Bellos](https://www.linkedin.com/in/filippos-bellos-168595156/)<sup>1</sup>, [Jason J. Corso](https://www.linkedin.com/in/jason-corso/)<sup>1,2</sup>
<sup>1</sup>University of Michigan, <sup>2</sup>Voxel51
[[Paper](https://arxiv.org/abs/2511.20525)] [[Code](https://github.com/yayuanli/MATT)] [[Project Page](https://yayuanli.github.io/MATT/)]
---
> **Dataset coming soon.** We are preparing the data for public release. Stay tuned!
## MATT-Bench Overview
MATT-Bench provides two large-scale benchmarks for **Mistake Attribution (MATT)** — a task that goes beyond binary mistake detection to attribute *what* semantic role was violated, *when* the mistake became irreversible (Point-of-No-Return), and *where* the mistake occurred in the frame.
The benchmarks are constructed by **MisEngine**, a data engine that automatically creates mistake samples with attribution-rich annotations from existing egocentric action datasets:
| Dataset | Samples | Instruction Texts | Semantic | Temporal | Spatial |
|---|---|---|---|---|---|
| **Ego4D-M** | 257,584 | 16,099 | ✓ | ✓ | ✓ |
| **EPIC-KITCHENS-M** | 221,094 | 12,283 | ✓ | — | — |
These are at least **two orders of magnitude larger** than any existing mistake dataset.
## Annotations
Each sample consists of an instruction text and an attempt video, annotated with:
- **Semantic Attribution**: Which semantic role (predicate, object) in the instruction is violated in the attempt video
- **Temporal Attribution**: The Point-of-No-Return (PNR) frame where the mistake becomes irreversible (Ego4D-M)
- **Spatial Attribution**: Bounding box localizing the mistake region in the PNR frame (Ego4D-M)
## Citation
```bibtex
@inproceedings{li2026mistakeattribution,
title = {Mistake Attribution: Fine-Grained Mistake Understanding in Egocentric Videos},
author = {Li, Yayuan and Jain, Aadit and Bellos, Filippos and Corso, Jason J.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2026},
}
```
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