# HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization arXiv: https://arxiv.org/abs/2401.09716 ยท HTML: https://mlsanity.com/paper/2401.09716/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 65 of its 91 references that are in the map. - First posted on arXiv 18 Jan 2024. ## Builds on (most cited first, 30 of 65) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021) - [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014) - [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015) - [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021) - [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020) - [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019) - [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022) - [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017) - [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020) - [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017) - [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015) - [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021) - [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021) - [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017) - [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020) - [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022) - [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021) - [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018) - [SWAD: Domain Generalization by Seeking Flat Minima](https://mlsanity.com/paper/2102.08604/) (2021) - [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020) ## Built on by (most cited first, 8 of 8) - [MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion](https://mlsanity.com/paper/2403.10568/) (2024) - [Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning](https://mlsanity.com/paper/2405.12217/) (2024) - [Federated Domain Generalization via Prompt Learning and Aggregation](https://mlsanity.com/paper/2411.10063/) (2024) - [Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts](https://mlsanity.com/paper/2510.19487/) (2025) - [Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning](https://mlsanity.com/paper/2605.08839/) (2026) - [Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization](https://mlsanity.com/paper/2504.21063/) (2025) - [FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization](https://mlsanity.com/paper/2505.02515/) (2025) - [Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers](https://mlsanity.com/paper/2609.18056/) (2026) ## Abstract (arXiv) Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel Hierarchical Contrastive Visual Prompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols.