mlsanityData to 14 Oct 2026

CLIP2TV: Align, Match and Distill for Video-Text Retrieval

arXiv 2111.05610 ↗First posted 10 Nov 2021cs.CV cs.CLcited by 14

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Abstract (arXiv)

Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head. With the success on both visual and textual representation learning, transformer based encoders and fusion methods have also been adopted in the field of video-text retrieval. In this report, we present CLIP2TV, aiming at exploring where the critical elements lie in transformer based methods. To achieve this, We first revisit some recent works on multi-modal learning, then introduce some techniques into video-text retrieval, finally evaluate them through extensive experiments in different configurations. Notably, CLIP2TV achieves 52.9@R1 on MSR-VTT dataset, outperforming the previous SOTA result by 4.1%.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 29 of its 37 references that are in the map. First posted on arXiv 10 Nov 2021.

Builds on

29 of 37 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  3. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,619
  4. 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,177
  5. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  6. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
  7. 2019HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clipscited by 950
  8. 2017Dense-Captioning Events in Videoscited by 793
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  10. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 665
  11. 2017Localizing Moments in Video with Natural Languagecited by 663
  12. 2021CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrievalcited by 651
17 more
  1. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 500
  2. 2021Less is More: ClipBERT for Video-and-Language Learning via Sparse Samplingcited by 484
  3. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 443
  4. 2019VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Researchcited by 414
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  7. 2019Unsupervised Label Noise Modeling and Loss Correctioncited by 328
  8. 2020ActBERT: Learning Global-Local Video-Text Representationscited by 267
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  10. 2020TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrievalcited by 215
  11. 2018A Joint Sequence Fusion Model for Video Question Answering and Retrievalcited by 208
  12. 2020Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoningcited by 180
  13. 2020Support-set bottlenecks for video-text representation learningcited by 170
  14. 2017Learning from Noisy Labels with Distillationcited by 161
  15. 2018Cross-Modal and Hierarchical Modeling of Video and Textcited by 103
  16. 2021TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignmentcited by 102
  17. 2021MDMMT: Multidomain Multimodal Transformer for Video Retrievalcited by 78

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Citing papers per year: 2023: 4; 2024: 6; 2025: 4.

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  1. 2024Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectivescited by 37
  2. 2023Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrievalcited by 35
  3. 2024Multi-granularity Correspondence Learning from Long-term Noisy Videoscited by 34
  4. 2023RGNet: A Unified Clip Retrieval and Grounding Network for Long Videoscited by 15
  5. 2025LoVR: A Benchmark for Long Video Retrieval in Multimodal Contextscited by 14
  6. 2024MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learningcited by 8
  7. 2024SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrievalcited by 6
  8. 2023STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-trainingcited by 5
  9. 2024VidLA: Video-Language Alignment at Scalecited by 5
  10. 2023Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understandingcited by 3
  11. 2025Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimationcited by 3
  12. 2024Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video Retrievalcited by 2
2 more
  1. 2025Image-to-Video Transfer Learning based on Image-Language Foundation Models: A Comprehensive Surveycited by 2
  2. 2025Frame-Difference Guided Dynamic Region Perception for CLIP Adaptation in Text-Video Retrievalcited by 0