Datasets › How2QA
How2QA
To collect How2QA for video QA task, the same set of selected video clips are presented to another group of AMT workers for multichoice QA annotation. Each worker is assigned with one video segment and asked to write one question with four answer candidates (one correctand three distractors). Similarly, narrations are hidden from the workers to ensure the collected QA pairs are not biased by subtitles. Similar to TVQA, the start and end points are provided for the relevant moment for each question. After filtering low-quality annotations, the final dataset contains 44,007 QA pairs for 22k 60-second clips selected from 9035 videos.
Source: HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Video Question Answering | How2QA | Text + Text (no Multimodal Pretext Training) Accuracy 93.2 | Towards Fast Adaptation of Pretrained Contrastive Models... | xudonglinthu/upgradable-multimodal-intelligence | 8 | Compare |
| Zero-Shot Learning | How2QA | SeViLA Accuracy 72.3 | — | — | 1 | Compare |
Papers archive 2025-07-28
5 shown of 5 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 28. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Zero-Shot Video Question Answering via Frozen Bidirectional Language Models | 3 | 2 | 16 Jun 2022 | ran 14 of 34 samples (20 unverified; 1 pointer-only for licence) |
| Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval | 1 | 1 | 5 Jun 2022 | ran 1 of 1 samples (0 unverified) |
| Revisiting the "Video" in Video-Language Understanding | 1 | 1 | 3 Jun 2022 | ran 3 of 5 samples (2 unverified) |
| Just Ask: Learning to Answer Questions from Millions of Narrated Videos | 1 | 2 | 1 Dec 2020 | not harvested |
| HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training | 3 | 1 | 1 May 2020 | ran 5 of 13 samples (8 unverified; 8 pointer-only for licence) |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- How2QA
1 variant name, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections