Add video-text-to-text task category and usage instructions

README.md

@@ -1,5 +1,11 @@
 ---
+language:
+- en
 license: mit
+size_categories:
+- 10K<n<100K
+task_categories:
+- video-text-to-text
 tags:
 - GUI
 - CUA
@@ -9,10 +15,6 @@ tags:
 - computer-use
 - video-demonstrations
 - desktop-automation
-language:
-- en
-size_categories:
-- 10K<n<100K
 ---
 
 <p align="center">
@@ -46,9 +48,36 @@ Unlike sparse screenshot datasets, VideoCUA preserves the full temporal dynamics
 
 VideoCUA is part of [CUA-Suite](https://cua-suite.github.io/), a unified ecosystem that also includes:
 
-- [**UI-Vision**](https://uivision.github.io/) — A rigorous desktop-centric benchmark evaluating element grounding, layout understanding, and action prediction.
+- [**UI-Vision**](https://uivision.github.io/) — A desktop-centric benchmark evaluating element grounding, layout understanding, and action prediction.
 - [**GroundCUA**](https://groundcua.github.io/) — A large-scale pixel-precise UI grounding dataset with 5M+ human-verified element annotations.
 
+## Usage
+
+To process the raw video data and action logs into trajectories for training or evaluation, you can use the synthesis pipeline provided in the [GitHub repository](https://github.com/ServiceNow/GroundCUA/tree/main/VideoCUA).
+
+### 1. Download & Extract
+```bash
+bash download_data.sh --repo ServiceNow/VideoCUA --output_dir ./VideoCUA
+```
+
+### 2. Convert to Trace Format
+To extract video frames at each action timestamp and produce standardized trajectories:
+```bash
+python convert_videocua.py \
+    --data_dir ./VideoCUA/data \
+    --output_dir ./videocua_processed \
+    --num_workers 4
+```
+
+### 3. Generate CoT Annotations
+```bash
+python gen_cot.py \
+    --task_list_path ./videocua_processed/task_list.json \
+    --model claude-sonnet-4.5 \
+    --num_threads 4 \
+    --suffix cot_v1
+```
+
 ## Repository Structure
 
 ```
@@ -99,7 +128,7 @@ Each application zip in `raw_data/` contains multiple task folders identified by
 }
 ```
 
-Each action entry includes a `groundcua_id` field — this is the unique identifier for the corresponding screenshot in the [GroundCUA](https://huggingface.co/datasets/ServiceNow/GroundCUA) repository. Using this ID, you can look up the fully annotated screenshot (with pixel-precise bounding boxes, textual labels, and semantic categories for every visible UI element) in GroundCUA, linking the video action trajectory to dense UI grounding annotations.
+Each action entry includes a `groundcua_id` field — this is the unique identifier for the corresponding screenshot in the [GroundCUA](https://huggingface.co/datasets/ServiceNow/GroundCUA) repository. Using this ID, you can look up the fully annotated screenshot in GroundCUA, linking the video action trajectory to dense UI grounding annotations.
 
 ## Citation