File size: 3,561 Bytes
26f24eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | # VANTAGE-Bench — `data/`
Brief overview of the dataset structure and per-task prompts.
Ground-truth answers are held server-side; only the **question side** of
each annotation ships here.
## Layout
```
data/
├── 2dbbox/ # 2D bounding-box detection
│ ├── prompt.json
│ └── <sequence>/images/*.jpg
├── dense_captioning/ # Dense video captioning
│ ├── prompt.json
│ └── *.mp4
├── event_verification/ # Binary event classification
│ └── filtered/
│ ├── metropolis_event_verification/{*.mp4, test_annotation.json}
│ ├── tailgating/{location_a, location_b}/{*.mp4, test_annotation.json}
│ └── warehouse_near_miss/{*.mp4, test_annotations.json}
├── pointing/ # 2D spatial pointing
│ └── Vantage2DPointing.tsv
├── referring/ # 2D referring expressions
│ └── refdrone_test_llava.json
├── temporal_localization/ # Temporal grounding
│ ├── *.mp4
│ └── data_jsons/annotations/*.json
├── tracking/ # Stateless single-object tracking
│ └── sot_benchmark.jsonl
└── vqa/ # Video question answering
├── *.mp4
└── data_jsons/annotations/*.json
```
## Per-task prompts
Tasks without a per-entry `question` field carry a top-level
`prompt.json` with the model instruction (schema: `{"prompt": "<text>"}`).
### `2dbbox/` — 2D Detection
> Locate every instance that belongs to the following categories: `person`. For each instance of the class, report bbox coordinates in JSON format. Do not group instances and report only individual instances. Avoid reporting duplicate instances.
### `dense_captioning/` — Dense Video Captioning
> Describe the notable events in the provided video. Provide the result in json format with `mm:ss.ff` format for time depiction for each event. Use keywords `start`, `end` and `caption` in the json output.
### `vqa/` — Video Question Answering
Per-entry questions in `vqa/data_jsons/annotations/*.json`. Each entry has `{q_uid, question, options, …}`; answer keys (`gt`, `gt_option`, `*_update_*`, etc.) are removed.
### `temporal_localization/` — Temporal Grounding
Per-entry questions in `temporal_localization/data_jsons/annotations/*.json`. Each entry has `{vid, question_id, question, duration, …}`; the `answer` timestamps are removed.
### `event_verification/` — Binary Event Verification
All four annotation files share a unified schema:
`{"bcq": [{id, video, system_prompt, question}, …]}`. The binary `answer`
is removed.
### `pointing/` — 2D Spatial Pointing
`Vantage2DPointing.tsv` — tab-separated. Each row carries the question and multiple-choice options; the last two ground-truth columns are dropped.
### `referring/` — 2D Referring Expressions
`refdrone_test_llava.json` — list of LLaVA conversation entries. Only the `human` turn (the question) is retained; the `gpt` turn (predicted bboxes) and GT meta fields are removed.
### `tracking/` — Stateless Single-Object Tracking
`sot_benchmark.jsonl` — one JSON object per clip with `seq_id`, `scene`, `camera`, `init_bbox` (the seed bounding box you're given as input), `init_frame_id`, and `canonical_frame_ids` (the frames you must predict at). Ground-truth trajectories are held server-side.
## Submitting predictions
See the top-level `README.md` for the eval-server instructions per task.
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