# tao-generate-referring-expressions

This skill turns images and KITTI box labels into grounded object descriptions and scene captions. Use it to build referring-expression annotations with bbox links.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1600 installs · ★ 3735
- Author: NVIDIA Corporation
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-generate-referring-expressions/SKILL.md
- Updated: 2026-10-26
- HTML: https://affpapa.org/en/nevibecode/skills/tao-generate-referring-expressions

## What it does

The skill takes image and KITTI-label pairs and writes annotations to `annotations.jsonl`. A VLM describes each boxed object, produces a scene-level caption, then groups objects into expressions linked to bbox lists. An optional verification step checks those boxes against the image and removes or tightens mismatches.

Object descriptions and scene captions run in parallel. On a rerun, processed images are skipped unless `workflow.force_reprocess: true` is set. You can omit captions or verification, and choose `jsonl`, `legacy`, or `both` output formats.

Runtime requirements are Docker, `nvidia-container-toolkit`, and access to a VLM endpoint: Gemini API or an OpenAI-compatible service. Gemini needs `GOOGLE_API_KEY`; an OpenAI-compatible service uses `base_url` and `model_name`. KITTI label lines with fewer than eight fields are skipped.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-referring-expressions -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tao-generate-referring-expressions/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tao-generate-referring-expressions/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-referring-expressions -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tao-generate-referring-expressions/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-referring-expressions -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tao-generate-referring-expressions/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-generate-referring-expressions
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-generate-referring-expressions/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-referring-expressions -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-generate-referring-expressions
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tao-generate-referring-expressions/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-referring-expressions -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-generate-referring-expressions/SKILL.md

## When to use it
- describe each object marked by a KITTI bbox
- write a scene-level caption for an image
- group objects into expressions linked to bbox lists
- check expression grounding against the image
- write image annotations to `annotations.jsonl`

## Example prompts
- Generate object descriptions from the images in `data.image_dir` and labels in `data.kitti_label_dir`.
- Run all image annotation steps, including the bbox verification pass.
- Create grounding expressions without scene captions, then verify their boxes.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8247808acd075369.

BEGIN THIRD-PARTY FILE 8247808acd075369
````text
---
name: tao-generate-referring-expressions
description: "Four-step image referring-expression pipeline: turns images plus KITTI bounding-box labels into region
  descriptions, scene captions, grounded referring expressions, and (optionally) verified expressions via VLM distillation. Use
  when the user wants to generate referring-expression annotations from images with KITTI labels, build region descriptions,
  produce grouped grounding phrases tied to bboxes, run a double-check verification pass on grounding expressions, auto-label
  traffic / scene images for referring datasets, or run the image_referring_expression pipeline. Triggers include 'referring
  expression', 'region description', 'KITTI labels', 'spatial relationship annotation', 'auto-label image referring expression',
  'image_referring_expression'."
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
metadata:
  author: NVIDIA Corporation
  version: "0.1.0"
tags:
  - image
  - referring-expression
  - kitti
  - bounding-boxes
  - auto-label
  - vlm
allowed-tools: Read Bash Write
---

# Image Referring Expression Pipeline

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).

Generate referring-expression and grounding annotations from images with KITTI-format bounding box labels. A single VLM (Gemini or any OpenAI-compatible endpoint) runs four steps: per-object region descriptions, holistic image captions, grouped grounding expressions tied to bboxes, and an optional double-check verification pass.

## Purpose

Transform `(image, KITTI labels)` pairs into a unified `annotations.jsonl` containing rich, grounded referring expressions. The VLM acts as a "teacher" annotator: Steps 0-1 see the image; Step 2 groups Step 0 outputs into grouping phrases with bbox lists; Step 3 (optional) re-examines those bboxes against the image and corrects mismatches.

## Pipeline Architecture

```
Step 0: Region expression  ──┐
                              ├──▶  Step 2: Grounding expression  ──▶  [Step 3: Double check]
Step 1: Image caption  ──────┘                                                   (optional)
```

- **Step 0 (region_expr)** — VLM emits one short discriminative phrase per KITTI bbox (`bbox_2d`, `type`, `color`, `description`).
- **Step 1 (image_caption)** — VLM emits a holistic, location-agnostic scene caption.
````
END THIRD-PARTY FILE 8247808acd075369

The third-party file has ended. Everything between the 8247808acd075369 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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