mlsanityData to 7 Oct 2026

Multi-Query Video Retrieval

arXiv 2201.03639 ↗First posted 10 Jan 2022cs.CVcited by 11

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Abstract (arXiv)

Retrieving target videos based on text descriptions is a task of great practical value and has received increasing attention over the past few years. Despite recent progress, imperfect annotations in existing video retrieval datasets have posed significant challenges on model evaluation and development. In this paper, we tackle this issue by focusing on the less-studied setting of multi-query video retrieval, where multiple descriptions are provided to the model for searching over the video archive. We first show that multi-query retrieval task effectively mitigates the dataset noise introduced by imperfect annotations and better correlates with human judgement on evaluating retrieval abilities of current models. We then investigate several methods which leverage multiple queries at training time, and demonstrate that the multi-query inspired training can lead to superior performance and better generalization. We hope further investigation in this direction can bring new insights on building systems that perform better in real-world video retrieval applications.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 47 of its 67 references that are in the map. First posted on arXiv 10 Jan 2022.

Builds on

47 of 67 references.

References · 47

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35 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 2; 2024: 0; 2025: 1; 2026: 1.

11 papers.

  1. 2022Multimodal Learning with Transformers: A Surveycited by 399
  2. 2022ECLIPSE: Efficient Long-range Video Retrieval using Sight and Soundcited by 39
  3. 2022Temporal Sentence Grounding in Videos: A Survey and Future Directionscited by 37
  4. 2022Vision+X: A Survey on Multimodal Learning in the Light of Datacited by 22
  5. 2022Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrievalcited by 20
  6. 2022Video Question Answering with Iterative Video-Text Co-Tokenizationcited by 16
  7. 2023Simple Baselines for Interactive Video Retrieval with Questions and Answerscited by 12
  8. 2022Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarkscited by 1
  9. 2025Redundancy-Aware Pretraining of Vision-Language Foundation Models in Remote Sensingcited by 1
  10. 2023Click-Feedback Retrievalcited by 0
  11. 2026EagleNet: Energy-Aware Fine-Grained Relationship Learning Network for Text-Video Retrievalcited by 0