Refine VPB dataset card style

README.md

@@ -10,30 +10,25 @@ tags:
 license: other
 ---
 
-# Video Progress Benchmark (VPB)
+# Video Progress Benchmark
 
-**A video-language benchmark for process-level robot task progress estimation.**
+<p align="center">
+  <strong>Process-level Task Progress Estimation for Robot Manipulation</strong>
+</p>
 
-[Paper](https://arxiv.org/abs/2607.09776) · [Code](https://github.com/InternRobotics/VLAC-cut) · [Model](https://huggingface.co/InternRobotics/VLAC-Cut) · [Benchmark](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark)
-
-The **Video Progress Benchmark (VPB)** evaluates whether a model can estimate how a robot task evolves throughout a video, not only whether the final frame looks successful. VPB is built from the held-out portion of the **Progress Annotation Dataset** and explicitly tests advancement, stagnation, regression, and recovery.
-
-This release provides the official split files and the raw-video subset required by the benchmark. Frame extraction, VLAC-Cut inference, and metric computation scripts are maintained in the GitHub repository.
-
-## Why VPB?
+<div align="center">
 
-Most existing robot reward and progress evaluations rely on endpoint success, isolated observations, frame pairs, or trajectories that are assumed to improve monotonically with time. Real robot executions are often different: a missed grasp, collision, drop, or wrong-object interaction can leave progress unchanged or move the task backward before a later recovery.
+[Paper](https://arxiv.org/abs/2607.09776) · [Code](https://github.com/InternRobotics/VLAC-cut) · [Model](https://huggingface.co/InternRobotics/VLAC-Cut) · [Benchmark](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark)
 
-VPB addresses this gap through two design choices:
+</div>
 
-1. **Continuous video context:** models evaluate temporally ordered robot videos rather than disconnected images.
-2. **Process-level evaluation:** metrics separately measure global trajectory recovery, terminal-state recognition, and local event-level direction.
+## Overview
 
-## Progress Annotation Dataset
+The **Video Progress Benchmark (VPB)** evaluates process-level task progress estimation for robot manipulation. It measures whether a model can capture advancement, stagnation, regression, and recovery throughout an execution video.
 
-Each annotated record contains a task instruction, a task-level plan, and task-relevant progress points. A progress point is attached to an observable semantic event such as contact formation or loss, grasp or placement outcome, object displacement, failure, stagnation, regression, or recovery.
+VPB is built from the held-out portion of the Progress Annotation Dataset. Unlike endpoint-only evaluations, VPB focuses on temporal task progress and supports analysis of partial completion, temporary failure, and subsequent recovery.
 
-When available, progress points also include auxiliary process supervision: scene state, robot action or object interaction, progress explanation, success/failure analysis, correction plan, and task-relevant grounding.
+This Hugging Face repository contains the official benchmark splits and video archives. The corresponding preprocessing, inference, and evaluation code is maintained in the GitHub repository.
 
 ## Dataset Scale
 
@@ -44,10 +39,7 @@ When available, progress points also include auxiliary process supervision: scen
 
 ## Official Splits
 
-VPB is organized along two axes:
-
-- **Seen vs. unseen:** whether the semantic task unit appears in the training split.
-- **Expert vs. non-expert:** whether the annotated trajectory contains a regressive progress transition.
+VPB is organized along two axes: whether the semantic task unit appears in the training split, and whether the annotated trajectory contains a regressive progress transition.
 
 | Split | Records | Episodes | Keyframe progress points |
 |---|---:|---:|---:|
@@ -59,23 +51,6 @@ VPB is organized along two axes:
 
 All views from the same physical execution are assigned to the same split. Held-out progress annotations are excluded from prompt construction, augmentation, in-context demonstration selection, fine-tuning, and checkpoint selection.
 
-## Evaluation Setup
-
-Annotated semantic keyframes are converted into a canonical reference trajectory by piecewise-linear interpolation. This interpolation is an evaluation convention; it does not assume that physical progress changes linearly between events.
-
-- global and terminal metrics are computed on the official `1 Hz` evaluation grid;
-- local direction metrics are computed directly on adjacent annotated semantic anchors;
-- global metrics are first computed per record and then averaged over metric-valid records, preventing long videos from dominating the result;
-- prediction coverage is strict by default in the public evaluator.
-
-### Metrics
-
-| Scope | Metrics | What they measure |
-|---|---|---|
-| Global trajectory | MAE, PRC, VOC | Absolute calibration and ordering of progress states; VOC is reported only for expert trajectories |
-| Terminal state | TSA, successful F1, failed/incomplete F1, Macro-F1 | Whether the final state is complete using the common `>= 90` threshold |
-| Local direction | AP+, AP-, MacroAP | Whether adjacent semantic events are correctly ranked as improvement or regression |
-
 ## Repository Contents
 
 ```text
@@ -107,7 +82,17 @@ __VLAC2_FRAMES_ROOT__/
 
 Resolve this prefix to the absolute extracted-frame directory when running the GitHub tools.
 
-## Evaluation Code
+## Evaluation Protocol
+
+Annotated semantic keyframes are converted into a canonical reference trajectory by piecewise-linear interpolation. This interpolation is an evaluation convention and does not assume that physical progress changes linearly between events.
+
+Global and terminal metrics are computed on the official `1 Hz` evaluation grid. Local direction metrics are computed directly on adjacent annotated semantic anchors. Global metrics are first computed per record and then averaged over metric-valid records so that long videos do not dominate the result.
+
+| Scope | Metrics | What they measure |
+|---|---|---|
+| Global trajectory | MAE, PRC, VOC | Absolute calibration and ordering of progress states; VOC is reported only for expert trajectories |
+| Terminal state | TSA, successful F1, failed/incomplete F1, Macro-F1 | Whether the final state is complete using the common `>= 90` threshold |
+| Local direction | AP+, AP-, MacroAP | Whether adjacent semantic events are correctly ranked as improvement or regression |
 
 Detailed running commands and the complete metric implementation are provided in the GitHub repository:
 
@@ -124,18 +109,20 @@ python scripts/evaluate_vpb_predictions.py \
 
 ## Citation
 
+Please cite the following paper when using VLAC-Cut, the released model, or the Video Progress Benchmark:
+
 ```bibtex
 @misc{zhai2026maximizinghumanefficiencylargescale,
-      title={Maximizing Human Efficiency in Large-Scale Robot Post-Training via VLAC-Cut Guided Pipeline}, 
-      author={Shaopeng Zhai and Qi Zhang and Tianyi Zhang and Haoran Zhang and Fuxian Huang and Zhanhui Lin and Zijun Xu},
-      year={2026},
-      eprint={2607.09776},
-      archivePrefix={arXiv},
-      primaryClass={cs.RO},
-      url={https://arxiv.org/abs/2607.09776}, 
+  title         = {Maximizing Human Efficiency in Large-Scale Robot Post-Training via VLAC-Cut Guided Pipeline},
+  author        = {Shaopeng Zhai and Qi Zhang and Tianyi Zhang and Haoran Zhang and Fuxian Huang and Zhanhui Lin and Zijun Xu},
+  year          = {2026},
+  eprint        = {2607.09776},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.RO},
+  url           = {https://arxiv.org/abs/2607.09776}
 }
 ```
 
 ## License
 
-See the license files distributed with this release. Third-party datasets and videos retain their original licenses and usage restrictions.
+The benchmark videos and third-party source data may be subject to additional licenses or terms of use. The source code in the GitHub repository is released under the MIT License.