mlsanityData to 7 Oct 2026

Deep Learning for Video-Text Retrieval: a Review

arXiv 2302.12552 ↗First posted 24 Feb 2023cs.CVcited by 15

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Abstract (arXiv)

Video-Text Retrieval (VTR) aims to search for the most relevant video related to the semantics in a given sentence, and vice versa. In general, this retrieval task is composed of four successive steps: video and textual feature representation extraction, feature embedding and matching, and objective functions. In the last, a list of samples retrieved from the dataset is ranked based on their matching similarities to the query. In recent years, significant and flourishing progress has been achieved by deep learning techniques, however, VTR is still a challenging task due to the problems like how to learn an efficient spatial-temporal video feature and how to narrow the cross-modal gap. In this survey, we review and summarize over 100 research papers related to VTR, demonstrate state-of-the-art performance on several commonly benchmarked datasets, and discuss potential challenges and directions, with the expectation to provide some insights for researchers in the field of video-text retrieval.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 101 of its 141 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

101 of 141 references.

References · 101

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 5.

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  1. 2024Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectivescited by 37
  2. 2025Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrievalcited by 26
  3. 2024Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognitioncited by 23
  4. 2025LoVR: A Benchmark for Long Video Retrieval in Multimodal Contextscited by 14
  5. 2026Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Frameworkcited by 8
  6. 2024SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrievalcited by 6
  7. 2024Towards Retrieval Augmented Generation over Large Video Librariescited by 4
  8. 2026ViSIL: Unified Evaluation of Information Loss in Multimodal Video Captioningcited by 2
  9. 2023FArMARe: a Furniture-Aware Multi-task methodology for Recommending Apartments based on the user interestscited by 1
  10. 2025Leveraging Auxiliary Information in Text-to-Video Retrieval: A Reviewcited by 1
  11. 2025Vote-in-Context: Turning VLMs into Zero-Shot Rank Fuserscited by 1
  12. 2025DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognitioncited by 1
3 more
  1. 2026Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysiscited by 0
  2. 2026U-CESE: Unified Clip-based Event Search Engine for AI Challenge HCMC 2025cited by 0
  3. 2026Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Datacited by 0