liner

README.md

@@ -62,25 +62,16 @@ MATT-Bench provides large-scale benchmarks for **Mistake Attribution (MATT)**
 
 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** | 220,800 | 19,467 | ✓ | ✓ | ✓ |
-| **EPIC-KITCHENS-M** | 299,715 | 12,283 | ✓ | — | — |
+| Dataset             | Samples | Instruction Texts | Semantic | Temporal | Spatial |
+|---------------------|---------|-------------------|----------|----------|---------|
+| **Ego4D-M**         | 220,800 | 19,467            | ✓        | ✓        | ✓       |
+| **EPIC-KITCHENS-M** | 299,715 | 12,283            | ✓        | —        | —       |
 
 These are at least **two orders of magnitude larger** than any existing mistake dataset. Instruction-text counts = unique (predicate `V`, argument `ARG1`) pairs.
 
 A third source, **HoloAssist-M**, is released alongside as an additional benchmark — see [Extended: HoloAssist-M](#extended-holoassist-m) below.
 
-## Annotations
-
-Each sample consists of an instruction text and an attempt video clip, annotated with:
-
-- **Semantic Attribution**: Which semantic role (predicate `V`, argument `ARG1`) in the instruction is violated in the attempt video.
-- **Temporal Attribution**: The Point-of-No-Return (PNR) frame where the mistake becomes irreversible. Inherited from Ego4D's native PNR annotations — available on **Ego4D-M only**.
-- **Spatial Attribution**: Bounding box localizing the mistake region in the PNR frame. Inherited from Ego4D's native bbox annotations — available on **Ego4D-M only**.
-
-## Repository Layout
-
+**Repository Layout**
 ```
 MATT-Bench/
 ├── ego4d/
@@ -101,8 +92,8 @@ MATT-Bench/
 
 MATT-Bench has two parts that you obtain separately:
 
-1. **Annotations** — hosted here, download via `hf` or `git clone`.
-2. **Video media** — **not** hosted here. Download from each source dataset using the instructions below. We only mirror the annotations; original videos retain their upstream licenses.
+1. **Annotations** — semantic attribution annotations are hosted here, download via `hf` or `git clone`. Temporal and spatial attribution annotations are inherited from the original dataset. 
+2. **Video media** — **not** hosted here. Download from each source dataset using the instructions below. Original videos retain their upstream licenses.
 
 ### Annotations (this repo)
 
@@ -124,59 +115,56 @@ epic_m  = load_dataset("mistakeattribution/MATT-Bench", "epickitchens")
 holo_m  = load_dataset("mistakeattribution/MATT-Bench", "holoassist")
 ```
 
-Or read the xlsx directly:
-
-```python
-import pandas as pd
-df = pd.read_excel("MATT-Bench/ego4d/train.xlsx")
-```
-
 ### Video media
 
-#### Ego4D (FHO benchmark clips only)
-
-MATT-Bench uses **only the FHO (Forecasting Hands and Objects) benchmark clips** from Ego4D, not the full 3,000-hour dataset.
+#### Ego4D 
 
-1. Sign the Ego4D license agreement at <https://ego4d.dev/request/ego4d> (approval ~48h; credentials expire 14 days after approval).
-2. Install the CLI (`pip install ego4d`) and download:
+Follow <https://ego4d-data.org/docs/CLI/> to download. The `video_uid` and `clip1_uid` fields in our annotations correspond to Ego4D's native video and clip UIDs.
 
-   ```bash
-   ego4d --output_directory="~/ego4d_data" --datasets clips --benchmarks FHO
-   ```
+MATT-Bench uses the FHO (Forecasting Hands and Objects) benchmark clips from Ego4D. Example downloading script:
+```bash
+ego4d --output_directory="~/ego4d_data" --datasets clips --benchmarks FHO
+```
 
-Docs: <https://ego4d-data.org/docs/CLI/>. The `video_uid` and `clip1_uid` fields in our annotations correspond to Ego4D's native video and clip UIDs; `start_frame` / `end_frame` are inherited from Ego4D's FHO annotations.
 
 #### EPIC-KITCHENS-100
 
-Standard EPIC-KITCHENS-100 download — no access form required.
+Follow <https://epic-kitchens.github.io/> to download. MATT-Bench's `video_id` matches EPIC's participant-video identifier (e.g. `P22_16`); `start_frame` / `end_frame` index the RGB frame sequence.
 
+Example download script:
 ```bash
 git clone https://github.com/epic-kitchens/epic-kitchens-download-scripts
 cd epic-kitchens-download-scripts
 python epic_downloader.py --rgb-frames    # or --videos
 ```
 
-Project page: <https://epic-kitchens.github.io/>. MATT-Bench's `video_id` matches EPIC's participant-video identifier (e.g. `P22_16`); `start_frame` / `end_frame` index the RGB frame sequence.
 
 #### HoloAssist
+Although not reported in the paper, we also support the HoloAssist dataset.
+
+Download the following from the [HoloAssist project page](https://holoassist.github.io/):
 
-Standard HoloAssist download — no access form required (CDLAv2 permissive license).
+| Resource               | Link                                                                                                                                     | Size      |
+|------------------------|------------------------------------------------------------------------------------------------------------------------------------------|-----------|
+| Videos (pitch-shifted) | [video_pitch_shifted.tar](https://hl2data.z5.web.core.windows.net/holoassist-data-release/video_pitch_shifted.tar)                       | 184.20 GB |
+| Labels                 | [data-annotation-trainval-v1_1.json](https://hl2data.z5.web.core.windows.net/holoassist-data-release/data-annotation-trainval-v1_1.json) | 111 MB    |
+| Dataset splits         | [data-splits-v1_2.zip](https://holoassist.github.io/label_files/data-splits-v1_2.zip)                                                    | —         |
 
-Direct download instructions: <https://holoassist.github.io/>. MATT-Bench's `video_id` matches HoloAssist's video identifier (e.g. `R076-21July-DSLR`).
+MATT-Bench's `video_id` matches HoloAssist's video identifier (e.g. `R076-21July-DSLR`).
 
 ## Data Schema
 
 ### `ego4d/{train,valid,test}.xlsx` — 13 columns
 
-| Column | Description |
-|---|---|
-| `video_uid` | Ego4D video UID (full video) |
-| `start_frame`, `end_frame` | Frame bounds of the attempt clip |
-| `clip1_uid`, `clip1_start_frame`, `clip1_end_frame` | Primary Ego4D clip |
-| `clip2_uid`, `clip2_start_frame`, `clip2_end_frame` | Paired clip for comparison (`Not required` / `-1` when absent) |
-| `V`, `ARG1` | Predicate and argument from the instruction (e.g. `plays`, `lawn tennis`) |
-| `label` | Mistake label |
-| `split` | Split identifier |
+| Column                                              | Description                                                                            |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------|
+| `video_uid`                                         | Ego4D video UID (full video)                                                           |
+| `start_frame`, `end_frame`                          | Frame bounds of the attempt clip                                                       |
+| `clip1_uid`, `clip1_start_frame`, `clip1_end_frame` | Primary Ego4D clip                                                                     |
+| `clip2_uid`, `clip2_start_frame`, `clip2_end_frame` | Some actions are distributed across two clips (`Not required` / `-1` when absent)      |
+| `V`, `ARG1`                                         | Predicate and argument from the instruction (e.g. `pick up`, `apple`)                  |
+| `label`                                             | Mistake label. 0: Correct; 1: Mistaken Predicate; 2: Mistaken Object; 3: Mistaken Both |
+| `split`                                             | dataset split identifier                                                                       |
 
 ### `ego4d/parquet.xlsx` — 29 columns (MisEngine reproduction data)
 
@@ -184,23 +172,23 @@ Ego4D narration-level records with semantic-role labels (`ARG0`, `V`, `ARG1`), f
 
 ### `epickitchens/{train,validation}.xlsx` and `holoassist/{train,validation}.xlsx` — 8 columns
 
-| Column | Description |
-|---|---|
-| `video_id` | Source-dataset video identifier |
-| `start_frame`, `end_frame` | Frame bounds of the attempt clip |
-| `V`, `ARG1` | Predicate and argument from the (possibly corrupted) instruction |
-| `label` | Mistake label |
-| `actual_V`, `actual_ARG1` | Ground-truth predicate/argument as performed |
+| Column                     | Description                                             |
+|----------------------------|---------------------------------------------------------|
+| `video_id`                 | Source-dataset video identifier                         |
+| `start_frame`, `end_frame` | Frame bounds of the attempt clip                        |
+| `V`, `ARG1`                | Predicate and argument of the instruction text          |
+| `label`                    | Mistake label                                           |
+| `actual_V`, `actual_ARG1`  | Predicate/argument of the action performed in the video |
 
-## Extended: HoloAssist-M
+### Extended: HoloAssist-M
 
 **HoloAssist-M** is an additional MATT benchmark released alongside MATT-Bench. It is **not** part of the main two-dataset evaluation reported in the CVPR 2026 paper; it uses the same MisEngine pipeline applied to the HoloAssist dataset.
 
-| Dataset | Samples | Instruction Texts | Semantic | Temporal | Spatial |
-|---|---|---|---|---|---|
-| **HoloAssist-M** | 562,209 | 1,786 | ✓ | — | — |
+| Dataset          | Samples | Instruction Texts | Semantic | Temporal | Spatial |
+|------------------|---------|-------------------|----------|----------|---------|
+| **HoloAssist-M** | 562,209 | 1,786             | ✓        | —        | —       |
 
-Schema matches EPIC-KITCHENS-M (semantic attribution only — HoloAssist does not provide native PNR or bbox annotations).
+Schema matches EPIC-KITCHENS-M (semantic attribution only — HoloAssist does not provide native PNR frame number andb bbox annotations).
 
 ## Citation