# LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning

arXiv: https://arxiv.org/abs/2402.17406 · HTML: https://mlsanity.com/paper/2402.17406/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 54 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [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)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Masked Feature Prediction for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2112.09133/) (2021)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark](https://mlsanity.com/paper/1910.04867/) (2019)
- [Self-Supervised Learning with Swin Transformers](https://mlsanity.com/paper/2105.04553/) (2021)
- [SiT: Self-supervised vIsion Transformer](https://mlsanity.com/paper/2104.03602/) (2021)
- [Fine-Grained Car Detection for Visual Census Estimation](https://mlsanity.com/paper/1709.02480/) (2017)
- [Improving Visual Prompt Tuning for Self-supervised Vision Transformers](https://mlsanity.com/paper/2306.05067/) (2023)
- [Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy](https://mlsanity.com/paper/2307.16867/) (2023)
- [The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods](https://mlsanity.com/paper/2104.03178/) (2021)

## Built on by (most cited first, 3 of 3)

- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [A Large-scale Medical Visual Task Adaptation Benchmark](https://mlsanity.com/paper/2404.12876/) (2024)
- [pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation](https://mlsanity.com/paper/2602.22938/) (2026)

## Abstract (arXiv)

Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnable tokens termed as prompts. Contemporary VPT methodologies, especially when employed with self-supervised vision transformers, often default to the introduction of new learnable prompts or gated prompt tokens predominantly sourced from the model's previous block. A pivotal oversight in such approaches is their failure to harness the potential of long-range previous blocks as sources of prompts within each self-supervised ViT. To bridge this crucial gap, we introduce Long-term Spatial Prompt Tuning (LSPT) - a revolutionary approach to visual representation learning. Drawing inspiration from the intricacies of the human brain, LSPT ingeniously incorporates long-term gated prompts. This feature serves as temporal coding, curbing the risk of forgetting parameters acquired from earlier blocks. Further enhancing its prowess, LSPT brings into play patch tokens, serving as spatial coding. This is strategically designed to perpetually amass class-conscious features, thereby fortifying the model's prowess in distinguishing and identifying visual categories. To validate the efficacy of our proposed method, we engaged in rigorous experimentation across 5 FGVC and 19 VTAB-1K benchmarks. Our empirical findings underscore the superiority of LSPT, showcasing its ability to set new benchmarks in visual prompt tuning performance.