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Datasets:
kfkas
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supermemory-vqa-imu-benchmark

Tasks:
Visual Question Answering
Modalities:
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parquet
Size:
1K - 10K
Tags:
egocentric-video
imu
streaming-vqa
benchmark-manifest
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supermemory-vqa-imu-benchmark / README.md
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Document common schema and provenance
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metadata 
pretty_name: SuperMemory-VQA
task_categories:
  - visual-question-answering
tags:
  - egocentric-video
  - imu
  - streaming-vqa
  - benchmark-manifest

SuperMemory-VQA

Viewer-ready benchmark manifest for kfkas/supermemory-vqa-imu-benchmark. It contains benchmark annotations and logical local asset hints, not redistributed source media.

Status

  • Base dataset: SuperMemory-VQA Aria stream
  • Rows: 4,853
  • Rows with local RGB+IMU: 4,818
  • Answer-key status: public=4853
  • Existing repository refreshed with the common schema.

Example video

A short, recompressed benchmark-corresponding clip is included for a quick visual check. It is not the complete source recording.

Open the example video

Common schema

All rows use the same columns: benchmark, base_dataset, question_id, category, question, choices, answer_index, answer_text, source_ids, query_time, evidence_time, clip_start_s, clip_end_s, stream_count, video_asset_hint, imu_asset_hint, local_video_available, local_imu_available, usable_for_imu_benchmark, coverage_status, answer_key_status, metadata_json, and streams.

streams uses a consistent nested shape for source ID, modality, sensor role, time bounds, asset hints, local availability, and join kind. Dataset-specific fields are encoded in metadata_json so the viewer schema remains stable.

Load 

from datasets import load_dataset

dataset = load_dataset("kfkas/supermemory-vqa-imu-benchmark", split="benchmark")
usable = dataset.filter(lambda row: row["usable_for_imu_benchmark"])

No full raw MP4, VRS, ZIP, CSV, or NPZ files are included. Only the short example clip is provided. Download complete source media from the upstream terms and use the relative hints under sensor_vqa/raw/.

Sources