Over 2 million images and videos form the core of the InstQA dataset, which also contains 6 million instance captions, 2 million image/video captions, and 10 million instance-level visual question answers. This dataset was created by wovenbytoyota-vai and was last updated on October 15, 2025. It is designed for instance-aware spatio-temporal visual question answering tasks.
Use Cases
- Training instance-aware visual question answering models based on the 10 million instance-level QA pairs.
- Developing models for generating dense, object-specific captions based on the 6 million instance captions.
- Benchmarking spatio-temporal reasoning in AI systems based on the dataset's video and image content.
- Pre-training multimodal large language models (MLLMs) on a large-scale corpus of aligned visual and textual data.
Strengths
- Large scale with over 2 million visual samples (images and videos).
- Dense annotations including 10 million instance-level visual question answers.
- Multi-faceted labeling with separate instance captions (6 million) and image/video captions (2 million).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Description metadata is limited; actual data quality and structure require manual inspection after download.
Provenance
- Source
- wovenbytoyota-vai
- Collection Method
- Likely curated and annotated for instance-aware visual understanding tasks.
- Time Range
- null
- Freshness
- Last updated 2025-10-15 08:33:13; freshness should be verified.
- Geography
- null