{
  "name": "VSI-Bench",
  "release_date": "2024-12-18",
  "subsets": {
    "mc": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_video_start"
      ],
      "task_type": "multiple_choice_qa",
      "score_pipeline": [
        "exact-match",
        "rule-match"
      ],
      "score_protocol": {
        "reference": "lmms-eval@lmms_eval/tasks/vsibench/utils.py:87-89,115-121 — MCA question types graded by fuzzy_matching (first token, strip trailing period) + case-insensitive exact match against the ground-truth letter; no LLM.",
        "note": "The org ships ONLY the multiple-choice question types (mc config, 2490 rows; confirmed in rows file). The official benchmark also contains NA (numerical) types graded with Mean Relative Accuracy MRA:.5:.95:.05 (utils.py:95-100,29-31), and the official headline 'overall' averages 8 per-question-type scores after averaging object_rel_direction easy/medium/hard (utils.py:158-170) — neither is reproducible from this copy or from a per-sample mean. Rows carry extra 'question_type' with VSI type names (object_rel_direction_hard, ...) that are not scorer vocabulary."
      },
      "prompt_template": "<video>These are frames of a video.\n{{ question }}\nOptions:\n{% for o in options %}{{ o }}\n{% endfor %}Answer with the option's letter from the given choices directly.",
      "prompt_template_source": {
        "origin": "official",
        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/vsibench/utils.py (vsibench_doc_to_text — canonical MCA prompt) + _default_template_yaml",
        "notes": "Tier 4: lmms-eval VSI-Bench canonical MCA prompt for the MC question types. SAT paper (arXiv 2412.07755, Appendix A.3, p.20) restricts VSI-Bench evaluation to the MC split — numerical splits (object_counting, object_size_estimation, room_size_estimation, object_abs_distance) are excluded. Each MC row carries the source `options` list verbatim from nyu-visionx/VSI-Bench under `choices`."
      },
      "video_storage": {
        "format": "files",
        "media_root": "videos",
        "notes": "512 source videos (243 referenced by the MC split): arkitscenes (121), scannet (79), scannetpp (43). Videos are mirrored from lccshunli/VSIBench at 5.7 GB total; nyu-visionx/VSI-Bench provides only QA metadata. Each scene_name resolves to videos/<dataset_source>/<scene_name>.mp4. Frame sampling is the inference backend's responsibility."
      },
      "mapping_from_source": {
        "media": {
          "from": "video_path",
          "type": "list",
          "min_items": 1,
          "max_items": 1
        },
        "id": {
          "from": "id"
        },
        "question": {
          "from": "question"
        },
        "answer": {
          "from": "ground_truth"
        },
        "choices": {
          "from": "options"
        },
        "extra": {
          "scene_name": {
            "from": "scene_name"
          },
          "dataset_source": {
            "from": "dataset"
          },
          "question_type": {
            "from": "question_type"
          }
        },
        "source": {
          "format": "huggingface",
          "url": {
            "test": "https://huggingface.co/datasets/nyu-visionx/VSI-Bench (QA metadata, 'full' config); videos mirrored from https://huggingface.co/datasets/lccshunli/VSIBench"
          }
        }
      }
    }
  }
}