# DGL: Dynamic Global-Local Prompt Tuning for Text-Video Retrieval

arXiv: https://arxiv.org/abs/2401.10588 · HTML: https://mlsanity.com/paper/2401.10588/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks](https://mlsanity.com/paper/2112.06825/) (2021)
- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [ActBERT: Learning Global-Local Video-Text Representations](https://mlsanity.com/paper/2011.07231/) (2020)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [A Dataset for Movie Description](https://mlsanity.com/paper/1501.02530/) (2015)
- [Unified Vision and Language Prompt Learning](https://mlsanity.com/paper/2210.07225/) (2022)
- [X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval](https://mlsanity.com/paper/2203.15086/) (2022)
- [TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval](https://mlsanity.com/paper/2207.07852/) (2022)
- [T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval](https://mlsanity.com/paper/2104.10054/) (2021)
- [Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?](https://mlsanity.com/paper/2301.00184/) (2022)
- [Disentangled Representation Learning for Text-Video Retrieval](https://mlsanity.com/paper/2203.07111/) (2022)
- [Cross Modal Retrieval with Querybank Normalisation](https://mlsanity.com/paper/2112.12777/) (2021)
- [A CLIP-Hitchhiker's Guide to Long Video Retrieval](https://mlsanity.com/paper/2205.08508/) (2022)
- [DiffusionRet: Generative Text-Video Retrieval with Diffusion Model](https://mlsanity.com/paper/2303.09867/) (2023)
- [Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning](https://mlsanity.com/paper/2303.14369/) (2023)
- [Local-Global Context Aware Transformer for Language-Guided Video Segmentation](https://mlsanity.com/paper/2203.09773/) (2022)
- [Cross-Modal Adapter for Vision-Language Retrieval](https://mlsanity.com/paper/2211.09623/) (2022)

## Built on by (most cited first, 26 of 26)

- [Dual-Personalizing Adapter for Federated Foundation Models](https://mlsanity.com/paper/2403.19211/) (2024)
- [VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing](https://mlsanity.com/paper/2502.17258/) (2025)
- [TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval](https://mlsanity.com/paper/2409.01156/) (2024)
- [FastVMT: Eliminating Redundancy in Video Motion Transfer](https://mlsanity.com/paper/2602.05551/) (2026)
- [KinMo: Kinematic-aware Human Motion Understanding and Generation](https://mlsanity.com/paper/2411.15472/) (2024)
- [STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding](https://mlsanity.com/paper/2503.15973/) (2025)
- [Continual Text-to-Video Retrieval with Frame Fusion and Task-Aware Routing](https://mlsanity.com/paper/2503.10111/) (2025)
- [TokenBinder: Text-Video Retrieval with One-to-Many Alignment Paradigm](https://mlsanity.com/paper/2409.19865/) (2024)
- [MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion](https://mlsanity.com/paper/2403.10568/) (2024)
- [Hierarchical Banzhaf Interaction for General Video-Language Representation Learning](https://mlsanity.com/paper/2412.20964/) (2024)
- [Uneven Event Modeling for Partially Relevant Video Retrieval](https://mlsanity.com/paper/2506.00891/) (2025)
- [DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval](https://mlsanity.com/paper/2506.08887/) (2025)
- [Hybrid-Tower: Fine-grained Pseudo-query Interaction and Generation for Text-to-Video Retrieval](https://mlsanity.com/paper/2509.04773/) (2025)
- [Neural Interaction Energy for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2404.16579/) (2024)
- [Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2024](https://mlsanity.com/paper/2406.12256/) (2024)
- [BiMa: Towards Biases Mitigation for Text-Video Retrieval via Scene Element Guidance](https://mlsanity.com/paper/2506.03589/) (2025)
- [State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding](https://mlsanity.com/paper/2510.12160/) (2025)
- [ProPy: Building Interactive Prompt Pyramids upon CLIP for Partially Relevant Video Retrieval](https://mlsanity.com/paper/2508.19024/) (2025)
- [FullTransNet: Full Transformer with Local-Global Attention for Video Summarization](https://mlsanity.com/paper/2501.00882/) (2025)
- [TAIL: Text-Audio Incremental Learning](https://mlsanity.com/paper/2503.04258/) (2025)
- [Learning Partially-Decorrelated Common Spaces for Ad-hoc Video Search](https://mlsanity.com/paper/2508.02340/) (2025)
- [MSAM: Multi-Semantic Adaptive Mining for Cross-Modal Drone Video-Text Retrieval](https://mlsanity.com/paper/2510.15470/) (2025)
- [SAVE: Speech-Aware Video Representation Learning for Video-Text Retrieval](https://mlsanity.com/paper/2603.08224/) (2026)
- [InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding](https://mlsanity.com/paper/2604.08337/) (2026)
- [Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis](https://mlsanity.com/paper/2605.00826/) (2026)
- [MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment](https://mlsanity.com/paper/2607.00858/) (2026)

## Abstract (arXiv)

Text-video retrieval is a critical multi-modal task to find the most relevant video for a text query. Although pretrained models like CLIP have demonstrated impressive potential in this area, the rising cost of fully finetuning these models due to increasing model size continues to pose a problem. To address this challenge, prompt tuning has emerged as an alternative. However, existing works still face two problems when adapting pretrained image-text models to downstream video-text tasks: (1) The visual encoder could only encode frame-level features and failed to extract global-level general video information. (2) Equipping the visual and text encoder with separated prompts failed to mitigate the visual-text modality gap. To this end, we propose DGL, a cross-modal Dynamic prompt tuning method with Global-Local video attention. In contrast to previous prompt tuning methods, we employ the shared latent space to generate local-level text and frame prompts that encourage inter-modal interaction. Furthermore, we propose modeling video in a global-local attention mechanism to capture global video information from the perspective of prompt tuning. Extensive experiments reveal that when only 0.67% parameters are tuned, our cross-modal prompt tuning strategy DGL outperforms or is comparable to fully finetuning methods on MSR-VTT, VATEX, LSMDC, and ActivityNet datasets. Code will be available at https://github.com/knightyxp/DGL