EgoMonth: Large-Scale Egocentric Video Question Answering Dataset

EgoMonth is a large-scale dataset of egocentric videos collected from wearable cameras. The dataset includes structured annotations for visual question answering, temporal reasoning, spatial reasoning, and long-term reasoning. It was created by anonymous-egomonth and last updated on Hugging Face in May 2026.

Use Cases

• Train visual question answering models based on the described QA pairs.
• Develop temporal reasoning models based on the described long-term video sequences.
• Build spatial reasoning models based on the described place and scene annotations.
• Benchmark multimodal AI systems on tasks requiring integration of visual and textual information.

Strengths

• Dataset is described as 'large-scale', suggesting substantial volume.
• Includes structured annotations for multiple complex reasoning tasks.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license information are unknown.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Collection Method

Collected from wearable cameras.

Freshness

Last updated 2026-05-05 11:01:14; freshness should be verified.