data/README.md

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
│   ├── *.mp4 (under videos/)
│   └── data_jsons/annotations/*.json
├── pointing/                     # 2D spatial pointing
│   └── VANTAGE_2DPointing.jsonl
├── 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 carries exactly three fields, scoped to model inference:

• q_uid — video/sample identifier; resolves against vqa/videos/
• question — natural-language question text
• options — list of MCQ answer choices used to build the prompt

Ground-truth (gt_option, answer) and per-question metadata (industry, event_type, task_type, dimension, start_time, end_time, video_duration) are not included in the public annotations.

temporal_localization/ — Temporal Grounding

Per-entry questions in temporal_localization/data_jsons/annotations/*.json. Each entry carries exactly three fields, scoped to model inference:

• vid — video identifier; resolves against temporal_localization/
• question_id — stable annotation identifier (reproducibility key)
• question — temporal-localization query

Ground-truth timestamps and per-question metadata (industry, event_type, task_type, duration) are not included in the public annotations.

event_verification/ — Binary Event Verification

Per-entry questions in event_verification/data_jsons/annotations/*.json (four files: VANTAGE_EventVerification.json — 67 entries, tailgating_location_a.json — 28, tailgating_location_b.json — 22, warehouse_near_miss.json — 46; 163 total). Each file is a top-level list of sample objects with schema [{id, video, system_prompt, question}, …] — matching the vqa/ and temporal_localization/ annotation layout — where video is the basename (e.g. example.mp4) and id is the stem (e.g. example), resolving against event_verification/videos/. The binary answer is removed.

pointing/ — 2D Spatial Pointing

VANTAGE_2DPointing.jsonl — one JSON object per line, 1,005 lines, 8 fields: index, question_id, image_path, question, A, B, C, D. Each line carries the question and four multiple-choice options (A–D); each option is an x,y pair (string "x,y") in the normalized 0–1000 coordinate system (both components in [0, 1000] relative to the image dimensions). index is an integer in [0, 1004]. Ground-truth fields (answer, target_point) are held server-side and are not included in the public JSONL.

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.