Tao Generate Referring Expressions
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The tao-generate-referring-expressions skill provides an automated pipeline for transforming images and KITTI-format bounding box labels into rich, grounded referring expressions. By leveraging a Vision-Language Model (VLM) as a teacher, the pipeline performs four distinct steps: generating region-specific descriptions, creating holistic scene captions, producing grouped grounding phrases tied to bounding boxes, and optionally verifying the accuracy of these expressions. This tool is designed to assist in auto-labeling traffic or scene images for training datasets, offering a structured approach to data annotation that supports both Gemini and OpenAI-compatible VLM endpoints.
What it does
- Generates discriminative region descriptions for objects defined by KITTI bounding boxes.
- Produces holistic, location-agnostic scene captions for input images.
- Groups objects into semantic phrases with associated bounding box lists.
- Performs an optional verification pass to tighten or remove inaccurate bounding boxes.
- Supports multiple VLM backends including Gemini and self-hosted OpenAI-compatible services.
- Outputs unified annotations in JSONL format for downstream machine learning tasks.
When to use it
- When you need to auto-label traffic or scene images for referring expression datasets.
- When you have existing KITTI-format bounding box labels and want to add descriptive text annotations.
- When you need to verify and refine existing grounding expressions for higher accuracy.
- When you want to build a dataset of grounded referring expressions tied to specific image regions.
Advantages
- Automates complex annotation tasks using powerful VLM capabilities.
- Provides a modular workflow where steps can be enabled or disabled based on requirements.
- Includes a built-in verification step to improve the quality of generated labels.
- Flexible integration with various VLM endpoints including local self-hosted servers.
- Supports both modern JSONL output and legacy formats for compatibility with existing tools.
Usage examples
- “Run the referring expression pipeline on my traffic images.”
- The agent will guide you through setting up the image and label directories, selecting a VLM endpoint, and executing the four-step annotation process.
- “How can I verify my existing grounding expressions?”
- The agent will configure the pipeline to run the Step 3 double-check verification pass on your existing annotations to identify and correct mismatches.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-generate-referring-expressions/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-generate-referring-expressions.zip -d .agents/skills
Documentation by NVIDIA Corporation
Image Referring Expression Pipeline
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill 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.
Step 2 (grounding_expr) — VLM groups Step 0 objects into grouping phrases and returns one bbox list per group, optionally using Step 1's caption as extra context.
Step 3 (double_check) — VLM re-checks each Step 2 bbox against the image; bad matches are removed, slightly-off boxes get tightened.
Steps 0 and 1 run in parallel within a single thread pool (they only depend on the seed records). Each step writes its own step_<N>_*/annotations.jsonl and skips already-processed images on re-run unless workflow.force_reprocess: true.
Instructions
Initial setup
When a user wants to run this pipeline, walk through these steps:
Images: Ask for
data.image_dir, the directory containing.jpg,.jpeg, or.pngimages.KITTI labels: Ask for
data.kitti_label_dir, the directory containing one.txtlabel file per image. Each label line must use KITTI format:<type> <truncated> <occluded> <alpha> <bbox_left> <bbox_top> <bbox_right> <bbox_bottom> .... Lines with fewer than 8 fields are silently skipped. Set this even for Step 1-only runs because Steps 0 and 2 require it.Resume from existing annotations: If the user already has a unified
annotations.jsonlfrom a previous run, setdata.input_annotations_jsonlto that file instead of seeding fromdata.image_diranddata.kitti_label_dir.API access: Ask the user which VLM endpoint they want to use. Present these five options and act on the choice:
Gemini — set
vlm.backend: "gemini"; requireGOOGLE_API_KEY(env var orvlm.gemini.api_key).NIM (e.g.
https://inference-api.nvidia.com/v1) — setvlm.backend: "openai"; collectbase_url,model_name, andapi_key.TAO inference microservice (self-hosted, OpenAI-compatible). Confirm whether the server is already running:
Running — collect
base_url,model_name, and (optionally)api_key; setvlm.backend: "openai".Not running — guide the user through the
skills/applications/tao-run-inference-serviceskill, which stands up a local TAO inference microservice with an OpenAI-compatible API. Before promising a specific model, checkskills/applications/tao-run-inference-service/references/service.yamlforvalid_network_arch_config_basenames. Once the server is up, collectbase_url,model_name, and (optionally)api_key; setvlm.backend: "openai".
vLLM (self-hosted, OpenAI-compatible). Confirm whether the server is already running:
Running — collect
base_url,model_name, and (optionally)api_key; setvlm.backend: "openai".Not running — follow references/vllm_server.md to install and launch a vLLM server, then collect
base_url,model_name, and (optionally)api_key; setvlm.backend: "openai".
Custom (any other OpenAI-compatible endpoint) — set
vlm.backend: "openai"; collectbase_url,model_name, and (optionally)api_key.
If the user has no endpoint and does not want to set one up, stop and help resolve API access first.
Workflow steps: Choose one of:
Full pipeline:
["0", "1", "2", "3"]No caption generation:
["0", "2", "3"], where Step 2 falls back to image-only contextNo verification:
["0", "1", "2"]Custom subset: any supported subset of steps
Output format: Choose one of:
jsonl: unified schema onlylegacy: byte-compatible.txt.stepNfiles onlyboth: writes both formats and is the default for downstream tooling
Running the pipeline
The pipeline runs inside the TAO Toolkit container via the auto_label CLI:
auto_label generate -e /path/to/spec.yaml \
results_dir=/results \
image_referring_expression.data.image_dir=/data/images \
image_referring_expression.data.kitti_label_dir=/data/labels \
image_referring_expression.vlm.gemini.api_key=$GOOGLE_API_KEYGenerate a default spec: auto_label default_specs results_dir=/results module_name=auto_label, then set autolabel_type: "image_referring_expression". All fields support Hydra dot-notation overrides on the command line.
See references/configuration.md for the full YAML structure, all parameters, model/endpoint setup, and error patterns.
Recommended pilot workflow
Run on 5-10 images with all four steps.
Inspect
step_0_region_expr/annotations.jsonl— are object types, colors, and discriminating phrases accurate?Inspect
step_2_grounding_expr/annotations.jsonl— are objects grouped sensibly, and do bbox coordinates match the described groups?Inspect
step_3_double_check/annotations.jsonl— were mismatched bboxes removed or tightened? Are any new errors introduced (rare)?If quality is insufficient, switch the VLM to a stronger model (e.g.
gemini-2.5-proor a larger Qwen3-VL endpoint), raisemedia_resolution/max_output_tokens, then re-run withworkflow.force_reprocess=true.Scale to the full dataset once satisfied.
Configuration
Key configuration fields (full reference in references/configuration.md):
| Field | Default | Description |
|---|---|---|
workflow.steps | ["0","1","2","3"] | Which steps to execute (0=region_expr, 1=image_caption, 2=grounding_expr, 3=double_check) |
workflow.max_workers | 4 | Parallel threads per step (watch API rate limits) |
workflow.force_reprocess | false | Ignore cached per-step outputs and reprocess from scratch |
workflow.output_format | "jsonl" (set to "both" in the default spec) | "jsonl", "legacy", or "both" |
vlm.backend | "gemini" | "gemini" or "openai" (OpenAI-compatible endpoint) |
data.image_dir | required | Directory of input images (.jpg / .jpeg / .png) |
data.kitti_label_dir | required (unless resuming) | Directory of KITTI-format .txt label files |
data.input_annotations_jsonl | "" | Optional pre-seeded annotations.jsonl (skips KITTI seeding) |
Inputs
Two ways to seed the pipeline:
Image directory + KITTI labels (default). Set
data.image_diranddata.kitti_label_dir. The orchestrator walks the image directory, reads the matching<stem>.txtKITTI file, parses bboxes (fields 0 + 4-7), reads each image'swidth/heightvia PIL, and writes aseed_annotations.jsonltoresults_dir/.Pre-seeded annotations JSONL (resume / pre-computed regions). Set
data.input_annotations_jsonlto a file with one{"image_id", "image_path", "width", "height", "kitti_bboxes": [...]}object per line.
Outputs
All outputs go to results_dir/:
seed_annotations.jsonl— initial per-image records (unlessinput_annotations_jsonlwas supplied).step_0_region_expr/annotations.jsonl— addsregions[](each withbbox/bbox_2d,type,color,description).step_1_image_caption/annotations.jsonl— addscaption(string).step_2_grounding_expr/annotations.jsonl— addsexpressions[](each{text, instances: [{bbox: [x1,y1,x2,y2]}]}).step_3_double_check/annotations.jsonl— same shape as Step 2, with bboxes removed/updated.results_dir/annotations.jsonl— copy of the last completed step's output.When
workflow.output_formatis"legacy"or"both", each step also writes byte-compatiblestep_<N>_*/labels/<stem>.txt.stepNfiles for the original 2d-data-engine tooling.
Prerequisites
Container:
nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt<!-- versions-key: images.tao_toolkit.pyt -->API access: At least one VLM endpoint (Gemini API key or OpenAI-compatible endpoint capable of image input)
PIL / Pillow: Required to read image dimensions during seeding (already present in the TAO container)
Files
- ATTRIBUTION.mdAdded by AutoGineer972 B
- BENCHMARK.md7.3 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.2 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json849 B
- references/configuration.md12.7 KiB
- references/skill_info.yaml1.5 KiB
- references/vllm_server.md6.8 KiB
- skill-card.md4.8 KiB
- skill.oms.sig5.4 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs an API key
The author says: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Instructions that use the network
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Attribution and licence
- Created by
- NVIDIA Corporation
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- NVIDIA (Official)
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- NVIDIA/skills · skills/tao-generate-referring-expressions · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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