# STA-VPT: SpatioTemporally Aligned Visual Prompt Tuning

arXiv: https://arxiv.org/abs/2312.10376 · HTML: https://mlsanity.com/paper/2312.10376/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts.
- Builds on 44 of its 68 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Placed in

- Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/)

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)

## Built on by (most cited first, 17 of 17)

- [Visual Fourier Prompt Tuning](https://mlsanity.com/paper/2411.01327/) (2024)
- [Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark](https://mlsanity.com/paper/2402.02242/) (2024)
- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers](https://mlsanity.com/paper/2505.23694/) (2025)
- [ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts](https://mlsanity.com/paper/2406.10973/) (2024)
- [Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts](https://mlsanity.com/paper/2501.18936/) (2025)
- [Proactive Schemes: A Survey of Adversarial Attacks for Social Good](https://mlsanity.com/paper/2409.16491/) (2024)
- [Adapter-Enhanced Semantic Prompting for Continual Learning](https://mlsanity.com/paper/2412.11074/) (2024)
- [Visual Prompt-Agnostic Evolution](https://mlsanity.com/paper/2601.20232/) (2026)
- [Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models](https://mlsanity.com/paper/2606.26379/) (2026)
- [IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks](https://mlsanity.com/paper/2412.16654/) (2024)
- [Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2412.16956/) (2024)
- [FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2603.08708/) (2026)
- [GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models](https://mlsanity.com/paper/2506.11042/) (2025)
- [Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers](https://mlsanity.com/paper/2512.24603/) (2025)
- [Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers](https://mlsanity.com/paper/2606.22383/) (2026)
- [Adapting Vision Foundation Models with Cascaded Semantics](https://mlsanity.com/paper/2608.05393/) (2026)

## Abstract (arXiv)

Typical methods for visual prompt tuning follow the sequential modeling paradigm originating from NLP, learning a sequence of unordered parameterized tokens as visual prompts, which are then prefixed to the flattened image representation for model adaptation. While such a sequential prompting paradigm has exhibited great promise, it presents two potential limitations. First, the learned visual prompts, presented in an unordered sequential form, are unable to capture the underlying spatial relations in the input image which are crucial for effective image encoding. Second, all prompt tokens serve the same role by performing uniform prompting for all image tokens without distinction, lacking fine-grained prompting capability---i.e., individualized prompting for different visual tokens to capture region-specific semantic patterns. In this work, we introduce the mymodel model (STA-VPT), a novel visual prompting paradigm, which learns a two-dimensional prompt token map for image prompting or a three-dimensional token volume for video prompting, ensuring spatial (or spatiotemporal) alignment with the input image token map (or video token volume). This alignment enables the visual prompts to preserve the spatial (or spatiotemporal) structure, thereby learning the underlying relations within the visual input. Furthermore, each prompt token serves as a specialized prompting expert and is designated to exclusively prompt for the spatially (or spatiotemporally) corresponding visual tokens. Consequently, our STA-VPT is capable of performing individualized prompting for different spatiotemporal regions, potentially improving the prompting performance through fine-grained allocation of prompting capacity, in line with mixture of experts (MoE) principles.