SuperMemory-VQA: Egocentric Visual Question Answering for AR Assistants

SuperMemory-VQA is a benchmark dataset containing 4,853 human-verified question-answer pairs for evaluating long-horizon memory in augmented reality assistants. The dataset is designed around practical questions a person might ask a wearable memory assistant, such as locating objects or recalling events. It was created by OSU-AIoT-MLSys-Lab and was last updated on June 5, 2026.

Use Cases

• Benchmarking long-horizon memory models based on questions about object location.
• Training wearable AI assistants to answer questions about past conversations or statements.
• Evaluating an assistant's ability to track task completion based on visual and temporal context.
• Developing models that can answer 'what happened next' questions about longer events.

Strengths

• Contains 4,853 human-verified question-answer pairs, providing a substantial evaluation set.
• Questions are grounded in practical, real-world scenarios for wearable memory assistants.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

OSU-AIoT-MLSys-Lab

Freshness

Last updated 2026-06-05 00:48:48; freshness should be verified.