EgoGazeVQA: Egocentric Gaze-Guided Video Question Answering Benchmark

EgoGazeVQA is an egocentric gaze-guided video question answering benchmark introduced in the paper 'In the Eye of MLLM: Benchmarking Egocentric Video Intent Understanding with Gaze-Guided Prompting'. The dataset, created by author taiyi09, leverages gaze information to improve the understanding of daily-life videos. It was last updated on 2026-01-22.

Use Cases

• Benchmarking video question answering models based on egocentric video and gaze data mentioned in the description
• Training models for intent understanding in daily-life activities based on the described egocentric video context
• Researching human attention and gaze patterns in first-person video scenarios described as 'daily-life videos'
• Developing gaze-guided prompting techniques for multimodal AI systems as referenced in the associated paper

Strengths

• Dataset is designed as a benchmark for a specific research problem, egocentric video intent understanding with gaze.
• Associated with a published research paper, providing academic context and methodology.
• Last updated on 2026-01-22, indicating recent maintenance.

Limitations

• Dataset size, row count, and specific file formats are unknown, which limits suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality and structure require manual inspection.

Provenance

Source

huggingface platform, uploaded by author taiyi09.

Collection Method

Introduced as a research benchmark; likely contains videos from sources like Ego4D and EgoExo as suggested by folder names.

Freshness

Last updated 2026-01-22 08:57:37; freshness should be verified.