tao-generate-referring-expressions

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.
• 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:

1. Images: Ask for data.image_dir, the directory containing .jpg, .jpeg, or .png images.

2. KITTI labels: Ask for data.kitti_label_dir, the directory containing one label file per image. Each label line must use KITTI format: . Lines with fewer than 8 fields are silently skipped. Set this even for Step 1-only runs because Steps 0 and 2 require it.