Image Referring Expression Pipeline

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 .txt label 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.
3. Resume from existing annotations: If the user already has a unified annotations.jsonl from a previous run, set data.input_annotations_jsonl to that file instead of seeding from data.image_dir and data.kitti_label_dir.
4. API access: Ask the user which VLM endpoint they want to use. Present these five options and act on the choice:
   1. Gemini — set vlm.backend: "gemini"; require GOOGLE_API_KEY (env var or vlm.gemini.api_key).
   2. NIM (e.g. https://inference-api.nvidia.com/v1) — set vlm.backend: "openai"; collect base_url, model_name, and api_key.
   3. TAO inference microservice (self-hosted, OpenAI-compatible). Confirm whether the server is already running:
      • Running — collect base_url, model_name, and (optionally) api_key; set vlm.backend: "openai".
      • Not running — guide the user through the skills/applications/tao-run-inference-service skill, which stands up a local TAO inference microservice with an OpenAI-compatible API. Before promising a specific model, check skills/applications/tao-run-inference-service/references/service.yaml for valid_network_arch_config_basenames. Once the server is up, collect base_url, model_name, and (optionally) api_key; set vlm.backend: "openai".
   4. vLLM (self-hosted, OpenAI-compatible). Confirm whether the server is already running:
      • Running — collect base_url, model_name, and (optionally) api_key; set vlm.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; set vlm.backend: "openai".
   5. Custom (any other OpenAI-compatible endpoint) — set vlm.backend: "openai"; collect base_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.

5. 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 context
   • No verification: ["0", "1", "2"]
   • Custom subset: any supported subset of steps
6. Output format: Choose one of:
   • jsonl: unified schema only
   • legacy: byte-compatible .txt.stepN files only
   • both: 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_KEY

Generate 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

1. Run on 5-10 images with all four steps.
2. Inspect step_0_region_expr/annotations.jsonl — are object types, colors, and discriminating phrases accurate?
3. Inspect step_2_grounding_expr/annotations.jsonl — are objects grouped sensibly, and do bbox coordinates match the described groups?
4. Inspect step_3_double_check/annotations.jsonl — were mismatched bboxes removed or tightened? Are any new errors introduced (rare)?
5. If quality is insufficient, switch the VLM to a stronger model (e.g. gemini-2.5-pro or a larger Qwen3-VL endpoint), raise media_resolution / max_output_tokens, then re-run with workflow.force_reprocess=true.
6. Scale to the full dataset once satisfied.

Configuration

Key configuration fields (full reference in references/configuration.md):

Inputs

Two ways to seed the pipeline:

1. Image directory + KITTI labels (default). Set data.image_dir and data.kitti_label_dir. The orchestrator walks the image directory, reads the matching <stem>.txt KITTI file, parses bboxes (fields 0 + 4-7), reads each image’s width/height via PIL, and writes a seed_annotations.jsonl to results_dir/.
2. Pre-seeded annotations JSONL (resume / pre-computed regions). Set data.input_annotations_jsonl to 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 (unless input_annotations_jsonl was supplied).
• step_0_region_expr/annotations.jsonl — adds regions[] (each with bbox/bbox_2d, type, color, description).
• step_1_image_caption/annotations.jsonl — adds caption (string).
• step_2_grounding_expr/annotations.jsonl — adds expressions[] (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_format is "legacy" or "both", each step also writes byte-compatible step_<N>_*/labels/<stem>.txt.stepN files for the original 2d-data-engine tooling.

Prerequisites

• Container: nvcr.io/nvidia/tao/tao-toolkit:6.26.3-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)