Individual skill
tao-generate-image-grounding
Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them to pixel-space bounding boxes via a VLM. Use when the user wants to ground captions to bboxes, generate phrase-grounded annotations, auto-label images for grounding, or run the image_grounding pipeline. Triggers include 'image grounding', 'phrase grounding', 'ground captions', 'auto-label image grounding', 'image_grounding'.
skills/tao-generate-image-grounding/SKILL.md
Source commit 25340619336e89bcc40365c28bfe9e790430468e
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- Last checked
- Nov 2, 2026
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- image Declared skill tag
- grounding Declared skill tag
- bounding-boxes Declared skill tag
- auto-label Declared skill tag
- vlm Declared skill tag
- 2d-grounding Declared skill tag
- engineering Derived category
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BENCHMARK.mddoc · 7,238 bytesSKILL.mdcore · 8,053 bytesSKILL.mdcore · 8,053 bytesconfig/skillspector-baseline.yamlasset · 834 bytesevals/evals.jsonasset · 819 bytesreferences/configuration.mddoc · 9,554 bytesreferences/skill_info.yamlasset · 1,080 bytesreferences/vllm_server.mddoc · 6,922 bytesskill-card.mddoc · 4,582 bytesskill.oms.sigasset · 5,569 bytes
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# Image Grounding 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).
Turn `(image, caption)` pairs into per-image grounded annotations: cleaned captions, referring expressions with character spans, and pixel-space bounding boxes for each expression. A single VLM (Gemini or any OpenAI-compatible endpoint) handles both steps.
## Purpose
Generate phrase-grounded training data for referring-expression and grounding models. The VLM acts as a "teacher" annotator: Step 0 extracts referring expressions from the caption while looking at the image; Step 1 returns one bbox set per expression for each image.
## Pipeline Architecture
```
Step 0: Expression extraction → VLM cleans caption, extracts referring expressions + char spans