Decoupling Augmentation Bias in Prompt Learning for Vision-Language Models

• URL: http://arxiv.org/abs/2511.03367v1
• Date: Wed, 05 Nov 2025 11:15:16 GMT
• Title: Decoupling Augmentation Bias in Prompt Learning for Vision-Language Models
• Authors: Gahyeon Kim, Sohee Kim, Seokju Lee,
• Abstract summary: Methods such as CoCoOp have shown that replacing handcrafted prompts with learnable vectors, known as prompt learning, can result in improved performance.<n>While traditional zero-shot learning techniques benefit from various data augmentation strategies, prompt learning has primarily focused on text-based modifications.<n>We explore how image-level augmentations, particularly those that introduce attribute-specific variations, can support and enhance prompt learning.
• Score: 8.634414503821697
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in large-scale vision and language models have led to significant progress in zero-shot learning tasks. Methods such as CoOp and CoCoOp have shown that replacing handcrafted prompts with learnable vectors, known as prompt learning, can result in improved performance. However, these models often struggle to generalize to entirely unseen categories. While traditional zero-shot learning techniques benefit from various data augmentation strategies, prompt learning has primarily focused on text-based modifications, leaving the potential of image-based augmentation largely unexplored. In this work, we explore how image-level augmentations, particularly those that introduce attribute-specific variations, can support and enhance prompt learning. Our analysis examines the interaction between these augmentations and soft prompt frameworks, revealing their potential to improve generalization. We also identify a limitation in existing methods, such as CoCoOp, which do not provide explicit guidance for learning prompts that focus on semantically meaningful visual features. To address this, we propose Adding Attributes to Prompt Learning, AAPL, a novel method that introduces adversarial token embeddings to decouple superficial visual variations introduced by augmentation from class-relevant semantic representations. This decoupling enables the learned prompts to concentrate on visually discriminative features that align with the target categories. We conduct comprehensive experiments on eleven benchmark datasets, and AAPL consistently outperforms existing methods across few-shot, zero-shot, cross-dataset, and domain generalization settings. Our source code is publicly available at: https://github.com/Gahyeonkim09/AAPL

Related papers

• Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning [58.73625654718187]
  Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes.<n>Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features.<n>This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation.
  arXiv  Detail & Related papers  (2025-03-29T10:17:57Z)
• InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models [24.170351966913557]
  We propose the InPK model, which infuses class-specific prior knowledge into the learnable tokens.<n>We also introduce a learnable text-to-vision projection layer to accommodate the text adjustments.<n>In experiments, InPK significantly outperforms state-of-the-art methods in multiple zero/few-shot image classification tasks.
  arXiv  Detail & Related papers  (2025-02-27T05:33:18Z)
• Spatio-Temporal Context Prompting for Zero-Shot Action Detection [13.22912547389941]
  We propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction.<n>To address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism.<n>Our method achieves superior results compared to previous approaches and can be further extended to multi-action videos.
  arXiv  Detail & Related papers  (2024-08-28T17:59:05Z)
• Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning [114.59476118365266]
  We propose AENet, which endows semantic information into the visual prompt to distill semantic-enhanced prompt for visual representation enrichment.<n> AENet comprises two key steps: 1) exploring the concept-harmonized tokens for the visual and attribute modalities, grounded on the modal-sharing token that represents consistent visual-semantic concepts; and 2) yielding semantic-enhanced prompt via the visual residual refinement unit with attribute consistency supervision.
  arXiv  Detail & Related papers  (2024-06-05T07:59:48Z)
• AAPL: Adding Attributes to Prompt Learning for Vision-Language Models [6.32186874112557]
  We propose adversarial token embedding to disentangle low-level visual augmentation features from high-level class information when inducing bias in learnable prompts. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.
  arXiv  Detail & Related papers  (2024-04-25T17:51:10Z)
• Boosting Audio-visual Zero-shot Learning with Large Language Models [32.533844163120875]
  We introduce a framework called KnowleDge-Augmented audio-visual learning (KDA) Our proposed KDA can outperform state-of-the-art methods on three popular audio-visual zero-shot learning datasets.
  arXiv  Detail & Related papers  (2023-11-21T01:18:23Z)
• Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models [64.24227572048075]
  We propose a Knowledge-Aware Prompt Tuning (KAPT) framework for vision-language models. Our approach takes inspiration from human intelligence in which external knowledge is usually incorporated into recognizing novel categories of objects.
  arXiv  Detail & Related papers  (2023-08-22T04:24:45Z)
• DPL: Decoupled Prompt Learning for Vision-Language Models [41.90997623029582]
  We propose a new method, Decoupled Prompt Learning, which reformulates the attention in prompt learning to alleviate this problem. Our approach is flexible for both visual and textual modalities, making it easily extendable to multi-modal prompt learning.
  arXiv  Detail & Related papers  (2023-08-19T15:48:38Z)
• SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification [84.05253637260743]
  We propose a new framework, named Semantic-guided Visual Adapting (SgVA), to extend vision-language pre-trained models. SgVA produces discriminative task-specific visual features by comprehensively using a vision-specific contrastive loss, a cross-modal contrastive loss, and an implicit knowledge distillation. State-of-the-art results on 13 datasets demonstrate that the adapted visual features can well complement the cross-modal features to improve few-shot image classification.
  arXiv  Detail & Related papers  (2022-11-28T14:58:15Z)
• CPL: Counterfactual Prompt Learning for Vision and Language Models [76.18024920393245]
  This paper presents a novel underlinetextbfCounterfactual underlinetextbfPrompt underlinetextbfLearning (CPL) method for vision and language models. CPL simultaneously employs counterfactual generation and contrastive learning in a joint optimization framework. Experiments demonstrate that CPL can obtain superior few-shot performance on different vision and language tasks.
  arXiv  Detail & Related papers  (2022-10-19T08:06:39Z)
• A Simple Long-Tailed Recognition Baseline via Vision-Language Model [92.2866546058082]
  The visual world naturally exhibits a long-tailed distribution of open classes, which poses great challenges to modern visual systems. Recent advances in contrastive visual-language pretraining shed light on a new pathway for visual recognition. We propose BALLAD to leverage contrastive vision-language models for long-tailed recognition.
  arXiv  Detail & Related papers  (2021-11-29T17:49:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.