mlsanityData to 14 Oct 2026

WildQA: In-the-Wild Video Question Answering

arXiv 2209.06650 ↗First posted 14 Sep 2022cs.CV cs.CLcited by 9

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Abstract (arXiv)

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https://lit.eecs.umich.edu/wildqa/.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 47 of its 86 references that are in the map. First posted on arXiv 14 Sep 2022.

Builds on

47 of 86 references.

References · 47

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35 more
  1. 2017TALL: Temporal Activity Localization via Language Querycited by 584
  2. 2019ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answeringcited by 561
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  9. 2015MovieQA: Understanding Stories in Movies through Question-Answeringcited by 304
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  11. 2020TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrievalcited by 215
  12. 2019TVQA+: Spatio-Temporal Grounding for Video Question Answeringcited by 154
  13. 2019Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answeringcited by 146
  14. 2015Exploring Models and Data for Image Question Answeringcited by 139
  15. 2014A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Inputcited by 112
  16. 2015Yin and Yang: Balancing and Answering Binary Visual Questionscited by 109
  17. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 106
  18. 2018Motion-Appearance Co-Memory Networks for Video Question Answeringcited by 95
  19. 2022Video Question Answering: Datasets, Algorithms and Challengescited by 84
  20. 2020Location-aware Graph Convolutional Networks for Video Question Answeringcited by 80
  21. 2021xGQA: Cross-Lingual Visual Question Answeringcited by 72
  22. 2017DeepStory: Video Story QA by Deep Embedded Memory Networkscited by 68
  23. 2016Leveraging Video Descriptions to Learn Video Question Answeringcited by 55
  24. 2019KnowIT VQA: Answering Knowledge-Based Questions about Videoscited by 53
  25. 2020DramaQA: Character-Centered Video Story Understanding with Hierarchical QAcited by 45
  26. 2016MarioQA: Answering Questions by Watching Gameplay Videoscited by 42
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  28. 2017Video Question Answering via Attribute-Augmented Attention Network Learningcited by 39
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 1; 2026: 1.

9 papers.

  1. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 135
  2. 2024VideoVista: A Versatile Benchmark for Video Understanding and Reasoningcited by 72
  3. 2023Reading Between the Lanes: Text VideoQA on the Roadcited by 23
  4. 2024Referring Atomic Video Action Recognitioncited by 16
  5. 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
  6. 2023Long Video Understanding with Learnable Retrieval in Video-Language Modelscited by 9
  7. 2024MixEval-X: Any-to-Any Evaluations from Real-World Data Mixturescited by 6
  8. 2026ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answeringcited by 1
  9. 2025VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generationcited by 0