# Tracking text prompts (v1) Fixed text prompts, such as `black hammer` or `silver alarm clock`, for every tracked object in RPX. In the paper they initialize the text-promptable trackers (Grounded SAM 2 and SAM 3.1) in place of a first-frame mask or box. There are 70 objects, each with one prompt of the form ` `. Prompts are given per clip, and there are 400 clips: 100 scenes × 3 phases (MOS) plus 100 egocentric recordings (Ego). That makes 2,739 rows, one per object per clip. A prompt stays the same for the whole clip, including frames where the object is occluded. Rows join to the released masks on `scene_id`, `kind`, `phase` and `mask_index`.

Usage · Files · How prompts are built · Known limitations · Reproducing

## Usage ```python import pandas as pd vocab = pd.read_parquet("scene_condition_vocab.parquet") clip = vocab.query("scene_id == 'scene001' and kind == 'mos' and phase == 1") # {1: 'tan lion', 2: 'white boot', ...} prompts = dict(zip(clip.mask_index, clip.prompt_text)) ``` `phase` is `0`, `1` or `2` (Clutter, Interaction, Clean) for MOS and empty for Ego. The `.jsonl` files hold the same rows as the Parquet files. ## Files | File | Rows | Contents | |:--|--:|:--| | `scene_condition_vocab.parquet` / `.jsonl` | 2,739 | One row per object per clip | | `object_catalog_vocab.parquet` / `.jsonl` | 70 | One row per object | | `manifest_summary.json` | | Counts, sources, validation, hashes | | `SHA256SUMS` | | SHA-256 of every file here | Clip rows hold `mask_index`, `prompt_text`, the object's identity and the annotation it was joined from. Object rows hold the name, all authored colors, materials, functions and categories, `primary_color` and `prompt_text`. `manifest_summary.json` also lists the known limitations below. ## How prompts are built `prompt_text` is `primary_color + " " + display_name`. `display_name` is the object's catalog name with underscores replaced by spaces (`air_duster_can` becomes `air duster can`). `primary_color` is the first color in the object's authored questionnaire, lowercased, with `gray` spelled `grey`. It is fixed per object, not the color that happens to be most visible in a given clip. Object identities come from the dataset's object catalog and each clip's released `mask_to_object.json`. The VQA attribute table is only used as a cross-check. ## Known limitations - 60 Ego mask entries are labeled `unknown`/`unlabeled` in the released annotations. They have no identity, so they get no prompt. They are listed in `manifest_summary.json`. - 252 of 658,254 visible Ego object occurrences (0.038%) use a mask index that has no identity at all in the released annotations. They fall in 14 of the 100 Ego clips, and 179 of them are index 8 in `scene058`. MOS is not affected. A tracker may therefore see a mask index in those clips with no matching row. - Colors are free text, so a `primary_color` can be `black and white`. An object has the same prompt in every scene it appears in. ## Reproducing ```bash cd benchmark/scripts/tracking_text_vocab python3 build_tracking_text_vocab.py --out-dir /path/to/out python3 validate_tracking_text_vocab.py --bundle-dir /path/to/out \ --report /path/to/out/validation_report.json ``` The validator exits nonzero on this bundle because of the 252 occurrences above; every other check passes. To confirm your run matches the release, compare its `missing_mappings_count` with the one in `manifest_summary.json`. The full coverage check streams every mask archive one clip at a time and deletes each when done, so disk use stays bounded. `--skip-exhaustive-masks` skips it for quick iteration; don't use it for a release check.
Design notes ### Why not use the VQA color questions `vqa/attribute.parquet` only asks an `attr_single_color` question when color alone identifies one visible object in that frame. It is a discrimination question, not a list of attributes. Building prompts from it would drop every object whose color is shared with something else on screen, or that was never the subject of a color question. Its answers also use questionnaire synonyms ("cleaner", "male toy", "analog alarm clock", "joystick") rather than the catalog name. Prompts are built instead from each object's catalog identity (`object_name` in `manifest/object_catalog_v1.json`) and its authored questionnaire (the color, material, function and category lists in `objects_meta//questionnaire.json`). Both exist for all 70 published objects. `vqa/attribute.parquet` is read only to cross-check the result and never fills in a missing attribute. ### Source revisions Each input is read at a fixed commit of the dataset: | Purpose | Revision | |:--|:--| | Catalog and questionnaires | `93e31d378f1f98eca18a7aa01a2279c9f332440c` | | MOS `sam2_meta` and `masks` tars | `2e2a387f7f93e98c177b2e039c141eacda94e5fc` | | Ego `sam2_meta` and `masks` tars | `f082723002bad5800dd85e583115b4ea05734d31` | | VQA cross-check | `vqa` branch | The catalog revision is the dataset's `main` branch when the bundle was built. The two tracking revisions predate the color, material and function questions; their catalog copies only have category and name for all 70 objects, so they can't be the attribute source. The MOS revision is `PINNED_DATASET_REVISION` in `benchmark/scripts/run_tracking.py` on this repo's `docker/tracking-all-yoloe` branch, the revision the tracking runner pins for MOS; the Ego revision is `PINNED_EGO_DATASET_REVISION` in the same file. The VQA cross-check reads `vqa/attribute.parquet` only. ### Join 1. `manifest/object_catalog_v1.json` lists the 70 published objects. Each entry's `source_catalog_id` is the join key. It is a string (for example `"18.2"`) and is never converted to a number. 2. Each entry's `questionnaire_path` points to `objects_meta//questionnaire.json`. Its `questions` are matched against fixed patterns: "made of" gives material, "used for" function, "color" color, "category" category and "name" name. This is the same parsing as `data/mask_annotation/visual_grounding_gt/vqa_gt/sos_catalog.py`, copied rather than imported so that this package has no dependency on the annotation code. 3. For each clip, `sam2/mask_to_object.json` inside `scenes///labels/sam2_meta/v1.tar` (MOS) or `scenes//ego/labels/sam2_meta/v1.tar` (Ego) maps each `mask_index` to `{"id": , "name": }`. Joining `id` to the catalog gives every tracked object in the clip its catalog name and attributes. 4. As a check, the object ids in `vqa/attribute.parquet` (non-null `target_oid` and the JSON-encoded `answer_oids`) are matched against the same catalog. ### Normalization - `colors_raw`, `materials_raw`, `functions_raw` and `categories_raw` keep the authored lists verbatim and in order. - `colors_normalized` is `colors_raw` lowercased and stripped, with internal whitespace collapsed and `gray` written as `grey`, the same rule as `_canon_color` in `gt_incontext.py`. - `primary_color` is always the first entry of `colors_normalized`. The full lists are kept next to it. - `display_name` is `canonical_name` lowercased with underscores replaced by spaces. None of the 70 `object_name` values carries an instance suffix (suffixes live only in `object_id` and `source_catalog_id`, e.g. `object_id="baking_tray.1"` with `object_name="baking_tray"`), and all 70 are distinct, so no suffix stripping is needed. - `prompt_text` is `f"{primary_color} {display_name}"`. ### `unknown`/`unlabeled` entries Some Ego `mask_to_object.json` files contain `{"id": "unknown", "name": "unlabeled"}` for a mask that was never given an identity. Across all 400 clips this happens for 60 of 2,799 entries, all of them Ego and all with exactly this pair; MOS has none, and there are no other unresolved ids. With no name or color there is nothing to build a prompt from, so these entries are left out of `scene_condition_vocab` and listed in `manifest_summary.json` and the validator's report. ### Ego mask indices with no identity The validator checks every mask in every one of the 98,124 MOS and Ego frames. It found 252 visible object occurrences (0.038% of 658,254) whose mask index has pixels in `labels/masks/v1.tar` but no entry in that clip's `mask_to_object.json`, not even the placeholder above. MOS is clean (0 of 75,000 frames). This is a gap in the published Ego annotations rather than in this pipeline. For example, `scene058`'s `mask_to_object.json` lists indices 1, 3–7 and 9 but not 8. Ego clips also have no `masks_aux` tar, which MOS uses as a second identity source, so there is nothing to build a prompt from without inventing an identity. 14 of the 100 Ego clips are affected. 179 of the 252 occurrences are index 8 in `scene058`, which is present in 179 of that clip's 182 frames. The other 73 are one- to three-frame blips across 13 scenes. Several of those use indices in the range `10001`–`10009`, which otherwise never occur in those clips, so they look like brief segmentation artifacts rather than missed objects. The full list of scenes, frames and indices is in `manifest_summary.json` under `coverage_and_validation.exhaustive_mask_coverage_check.missing_mappings` and in the validator's report. The bundle covers every mask index that has an identity: all of MOS and 99.96% of Ego occurrences. ### One prompt per clip, not per frame `scene_condition_vocab` has one row per object per scene, kind and phase. A promptable tracker is usually initialized once at the start of a clip and needs a prompt for every object it may have to follow, including one that is occluded for a while and comes back. The per-frame mask check in `validate_tracking_text_vocab.py` only proves that every mask index appearing in any frame is covered by these clip-level rows. The bundle does not provide per-frame prompts. ### Authored colors - Colors are free-text questionnaire answers, not a fixed palette. Values such as `black and white`, `grey and yellow` or `white black red and blue` are real answers and appear verbatim in `primary_color` and `prompt_text` when they come first. - `primary_color` is the first authored color. It is not necessarily the dominant or most visible color under a particular clip's lighting. - Attributes belong to the object, not to a scene, so the same physical object has the same prompt everywhere it appears.
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