# Advancing Textual Prompt Learning with Anchored Attributes

arXiv: https://arxiv.org/abs/2412.09442 · HTML: https://mlsanity.com/paper/2412.09442/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 69 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 30 of 53)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [Prompt-aligned Gradient for Prompt Tuning](https://mlsanity.com/paper/2205.14865/) (2022)
- [Visual Classification via Description from Large Language Models](https://mlsanity.com/paper/2210.07183/) (2022)
- [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://mlsanity.com/paper/2303.13283/) (2023)

## Built on by (most cited first, 20 of 20)

- [Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2603.05235/) (2026)
- [NAIPv2: Debiased Pairwise Learning for Efficient Paper Quality Estimation](https://mlsanity.com/paper/2509.25179/) (2025)
- [Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning](https://mlsanity.com/paper/2603.13341/) (2026)
- [Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models](https://mlsanity.com/paper/2508.02671/) (2025)
- [SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking](https://mlsanity.com/paper/2511.04711/) (2025)
- [VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2511.22664/) (2025)
- [Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models](https://mlsanity.com/paper/2606.26379/) (2026)
- [AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning](https://mlsanity.com/paper/2511.21188/) (2025)
- [Prompt Tuning for CLIP on the Pretrained Manifold](https://mlsanity.com/paper/2602.19198/) (2026)
- [DeAR: Fine-Grained VLM Adaptation by Decomposing Attention Head Roles](https://mlsanity.com/paper/2603.01111/) (2026)
- [FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2603.08708/) (2026)
- [SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection](https://mlsanity.com/paper/2605.23144/) (2026)
- [Robust Prompt Tuning for Vision-Language Models with Mild Semantic Noise](https://mlsanity.com/paper/2508.04677/) (2025)
- [Decoupling Augmentation Bias in Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2511.03367/) (2025)
- [MERGETUNE: Continued Fine-Tuning of Vision-Language Models](https://mlsanity.com/paper/2601.10497/) (2026)
- [SteelDefectX: A Multi-Form Vision-Language Dataset and Benchmark for Steel Surface Defect Analysis](https://mlsanity.com/paper/2603.21824/) (2026)
- [SpecPL: Disentangling Spectral Granularity for Prompt Learning](https://mlsanity.com/paper/2605.04504/) (2026)
- [FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models](https://mlsanity.com/paper/2605.28347/) (2026)
- [Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition](https://mlsanity.com/paper/2607.06185/) (2026)
- [PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning](https://mlsanity.com/paper/2608.13969/) (2026)

## Abstract (arXiv)

Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text inputs, aiming to align image and text (category) spaces for downstream tasks. However, current training is restricted to aligning images with predefined known categories and cannot be associated with unknown categories. In this work, we propose utilizing universal attributes as a bridge to enhance the alignment between images and unknown categories. Specifically, we introduce an Attribute-anchored Textual Prompt learning method for vision-language models, named ATPrompt. This approach expands the learning space of soft prompts from the original one-dimensional category level into the multi-dimensional attribute level by incorporating multiple attribute tokens into the learnable soft prompts. Through this modification, we transform the text prompt from a category-centric form to an attribute-category hybrid form. Additionally, we introduce a straightforward differentiable attribute search method to identify representative and suitable attributes for downstream tasks. As an easy-to-use plug-in technique, ATPrompt can seamlessly replace the existing basic prompt format in textual-based methods, providing general improvements at a negligible computational cost. Extensive experiments across 11 datasets validate the effectiveness of our method. Code is publicly available at https://github.com/zhengli97/ATPrompt.