polish readme

README.md

@@ -2,22 +2,34 @@
 license: other
 license_name: nvidia-evaluation-data-license
 license_link: LICENSE
-pretty_name: VANTAGE-Bench
 language:
 - en
+pretty_name: VANTAGE-Bench
+size_categories:
+- 10K<n<100K
 task_categories:
 - visual-question-answering
 - video-text-to-text
-size_categories:
-- 10K<n<100K
+- image-text-to-text
+- object-detection
+- multiple-choice
+task_ids:
+- visual-question-answering
+- image-captioning
+- multiple-choice-qa
 tags:
 - video
+- image
+- text
+- multimodal
 - video-understanding
+- image-understanding
 - benchmark
 - evaluation
 - infrastructure-cameras
 - warehouse
 - smart-city
+- intelligent-transportation-systems
 - smart-spaces
 configs:
 - config_name: vqa
@@ -54,38 +66,44 @@ configs:
     path: data/dense_captioning/metadata.jsonl
 ---
 
-# VANTAGE-BENCH
+# VANTAGE-Bench
 
 *Video ANalysis Tasks Across Generalized Environments*
 
 **3 domains · 8 tasks · 35,027 annotations · 3,346 media samples · 42 GB**
 
-<img src="./assets/vantage_bench_tasks.png" alt="VANTAGE-BENCH task overview across Semantic, Temporal, Spatial, and Spatio-Temporal understanding categories" width="100%">
+<img src="./assets/vantage_bench_tasks.png" alt="VANTAGE-Bench task overview across Semantic, Temporal, Spatial, and Spatio-Temporal understanding categories" width="100%">
 
 ## Dataset Description
 
-VANTAGE-BENCH is the first public benchmark purpose-built for evaluating visual understanding on video captured by fixed infrastructure cameras. It spans three real-world domains — warehouse, smart city / Intelligent Transportation Systems (ITS), and smart spaces — across 8 tasks spanning semantic, temporal, spatial, and spatio-temporal evaluation, including video question answering (VQA), temporal grounding, dense video captioning, event verification, spatial grounding, and spatio-temporal tracking. Unlike ordinary web video, this footage comes from fixed, infrastructure-mounted viewpoints — persistent scenes under long-duration monitoring that demand reasoning over stationary warehouse, ITS, and smart-space environments.
+VANTAGE-Bench is the first public benchmark purpose-built for evaluating visual understanding on video captured by fixed infrastructure cameras. It spans three real-world domains — warehouse, smart city / Intelligent Transportation Systems (ITS), and smart spaces — across 8 tasks spanning semantic, temporal, spatial, and spatio-temporal evaluation, including video question answering (VQA), temporal localization, dense video captioning (DVC), event verification, spatial pointing, referring expressions, and spatio-temporal tracking. Unlike ordinary web video, this footage comes from fixed, infrastructure-mounted viewpoints — persistent scenes under long-duration monitoring that demand reasoning over stationary warehouse, ITS, and smart-space environments.
 
 > **Evaluation-only / test split.** Ground-truth answers are withheld and all scoring is performed server-side — this dataset repository does not provide local benchmark scoring.
 
 ### Directory Structure
 
 ```text
-VANTAGE-BENCH/
-├── vqa/                     # Video question answering
-├── dense_captioning/        # Dense video captioning
-├── temporal_localization/   # Temporal localization
-├── event_verification/      # Event verification
-├── 2dbbox/                  # 2D object localization
-├── referring/               # 2D referring expressions
-├── pointing/                # 2D spatial pointing
-├── tracking/                # Spatio-temporal tracking
-└── README.md                # Dataset documentation and submission instructions
+PhysicalAI-VANTAGE-Bench/
+├── data/
+│   ├── 2dbbox/                # 2D object localization
+│   ├── dense_captioning/      # Dense video captioning
+│   ├── event_verification/    # Event verification
+│   ├── pointing/              # 2D spatial pointing
+│   ├── referring/             # 2D referring expressions
+│   ├── temporal_localization/ # Temporal localization
+│   ├── tracking/              # Single object tracking
+│   └── vqa/                   # Video question answering
+│
+├── scripts/
+│   ├── run_lmudata.py         # Prepare benchmark datasets
+│   └── RUN_LMUData.md         # Setup and usage guide
+│
+└── README.md                  # Dataset documentation
 ```
 
 ## Get Started
 
-VANTAGE-Bench is a multi-component benchmark; this repository is its **data and schema layer**. For everything else, use the matching component:
+This repository contains the official VANTAGE-Bench dataset and data schemas. For benchmark documentation, submissions, and leaderboard results, use the resources below:
 
 - **[VANTAGE-Bench's official website](https://vantage-bench.org/)** — detailed overview of VANTAGE-Bench, the benchmark suite, and submission entry points.
 - **[VANTAGE-Bench GitHub repository](https://github.com/Clemson-Capstone/VANTAGE-Bench)** — run guides, inference workflows, submission formats, and benchmark tooling.
@@ -94,9 +112,9 @@ VANTAGE-Bench is a multi-component benchmark; this repository is its **data and
 ## Quick Start
 
 This repository ships the **test-split media and question-side annotations**;
-ground-truth answers are withheld for server-side scoring. To build an
-inference-ready (no-ground-truth) LMUData layout for VLMEvalKit across every
-task:
+ground-truth answers are withheld for server-side scoring. VANTAGE-Bench's evaluation 
+toolkit expects benchmark datasets to be organized using a standard directory structure 
+called LMUData. To build an inference-ready LMUData layout across every task:
 
 ```bash
 python scripts/run_lmudata.py --all --lmu-root ~/LMUData
@@ -114,7 +132,7 @@ requirements, per-task notes, and troubleshooting.
 
 `run_lmudata.py` automates the inference-prep step end to end. It sources the
 public dataset (an auto-detected local `data/` clone, an explicit
-`--local-source`, or a Hugging Face snapshot), builds each task's VLMEvalKit
+`--local-source`, or a Hugging Face snapshot), builds each task's
 index file (`*.tsv` / `annotations.json`), and places the media by symlink
 (default) or `--copy`. It writes **no** ground-truth fields — withheld answers
 are left empty — and is idempotent, so re-runs only fill in what is missing.
@@ -126,7 +144,7 @@ which the script handles automatically when they are met:
 - **2D Referring Expressions (grounding)** — downloads the RefDrone / VisDrone images over the network.
 
 Under `--all`, a task that cannot meet its prerequisites is skipped while the
-others continue. The result is a VLMEvalKit-ready layout under
+others continue. The result is a inference-ready layout under
 `<LMUData root>/datasets/`:
 
 ```text
@@ -188,7 +206,7 @@ The end-to-end workflow is:
    python scripts/run_lmudata.py --all --lmu-root ~/LMUData
    ```
 
-2. Run VLMEvalKit inference with `--mode infer`. Each run emits a
+2. Run inference using VANTAGE-Bench's evaluation toolkit. Each run emits a
    `*.submission.jsonl` of predictions.
 3. Submit the predictions through the flow documented on
    [VANTAGE-Bench's official website](https://vantage-bench.org/) and in the