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Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigation

Shohei Enomoto

TL;DR

Visual prompting is a parameter-efficient technique but limited by additive transformations and overfitting at larger prompt sizes. The authors propose ACAVP, which adds affine and color transformations to extend the transformation space and uses TrivialAugment to mitigate overfitting. Empirical results across twelve datasets and two backbones show ACAVP achieves state-of-the-art VP accuracy, surpasses linear probing on average, and exhibits superior robustness to distribution shifts with only modest inference overhead. A theoretical PAC-style analysis and ablation studies support the design choices and underline the importance of expressive transformations and regularization in VP.

Abstract

Visual prompting (VP) has emerged as a promising parameter-efficient fine-tuning approach for adapting pre-trained vision models to downstream tasks without modifying model parameters. Despite offering advantages like negligible computational overhead and compatibility with black-box models, conventional VP methods typically achieve lower accuracy than other adaptation approaches. Our analysis reveals two critical limitations: the restricted expressivity of simple additive transformation and a tendency toward overfitting when the parameter count increases. To address these challenges, we propose ACAVP (Affine, Color, and Additive Visual Prompting), which enhances VP's expressive power by introducing complementary transformation operations: affine transformation for creating task-specific prompt regions while preserving original image information, and color transformation for emphasizing task-relevant visual features. Additionally, we identify that overfitting is a critical issue in VP training and introduce TrivialAugment as an effective data augmentation, which not only benefits our approach but also significantly improves existing VP methods, with performance gains of up to 12 percentage points on certain datasets. This demonstrates that appropriate data augmentation is universally beneficial for VP training. Extensive experiments across twelve diverse image classification datasets with two different model architectures demonstrate that ACAVP achieves state-of-the-art accuracy among VP methods, surpasses linear probing in average accuracy, and exhibits superior robustness to distribution shifts, all while maintaining minimal computational overhead during inference.

Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigation

TL;DR

Visual prompting is a parameter-efficient technique but limited by additive transformations and overfitting at larger prompt sizes. The authors propose ACAVP, which adds affine and color transformations to extend the transformation space and uses TrivialAugment to mitigate overfitting. Empirical results across twelve datasets and two backbones show ACAVP achieves state-of-the-art VP accuracy, surpasses linear probing on average, and exhibits superior robustness to distribution shifts with only modest inference overhead. A theoretical PAC-style analysis and ablation studies support the design choices and underline the importance of expressive transformations and regularization in VP.

Abstract

Visual prompting (VP) has emerged as a promising parameter-efficient fine-tuning approach for adapting pre-trained vision models to downstream tasks without modifying model parameters. Despite offering advantages like negligible computational overhead and compatibility with black-box models, conventional VP methods typically achieve lower accuracy than other adaptation approaches. Our analysis reveals two critical limitations: the restricted expressivity of simple additive transformation and a tendency toward overfitting when the parameter count increases. To address these challenges, we propose ACAVP (Affine, Color, and Additive Visual Prompting), which enhances VP's expressive power by introducing complementary transformation operations: affine transformation for creating task-specific prompt regions while preserving original image information, and color transformation for emphasizing task-relevant visual features. Additionally, we identify that overfitting is a critical issue in VP training and introduce TrivialAugment as an effective data augmentation, which not only benefits our approach but also significantly improves existing VP methods, with performance gains of up to 12 percentage points on certain datasets. This demonstrates that appropriate data augmentation is universally beneficial for VP training. Extensive experiments across twelve diverse image classification datasets with two different model architectures demonstrate that ACAVP achieves state-of-the-art accuracy among VP methods, surpasses linear probing in average accuracy, and exhibits superior robustness to distribution shifts, all while maintaining minimal computational overhead during inference.

Paper Structure

This paper contains 41 sections, 3 theorems, 13 equations, 6 figures, 16 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Transformation design for VP.
  4. Learning strategies for VP.
  5. Proposed Method: ACAVP
  6. Preliminaries
  7. Problem Analysis
  8. ACAVP Design
  9. Overfitting Mitigation
  10. Experiments
  11. Main Results
  12. Robustness to Distribution Shift
  13. Ablation Study
  14. Effectiveness of Overfitting Mitigation Strategies
  15. Computational Efficiency Analysis
  16. ...and 26 more sections

Key Result

Theorem B.1

Given an input space $X$, a discrete label space $Y$, and a distribution $D$ over $X \times Y$, if there are two hypothesis spaces $F_1 \subseteq \{f : X \rightarrow Y\}$ and $F_2 \subseteq \{f : X \rightarrow Y\}$ satisfying $F_1 \subseteq F_2$, then we have

Figures (6)

  • Figure 1: Overview of ACAVP. The input image undergoes three transformations: first, an affine transformation using the affine prompt, followed by a color transformation using the color prompt, and finally an additive transformation using the additive prompt. The resulting VP image is fed into a frozen recognition model, and the loss is backpropagated to update all three prompt parameters.
  • Figure 2: Visualization analysis of different VP methods. The first row shows transformed images on the CIFAR10 dataset using CLIP ViT-B/32. The second row presents transformed images on the CIFAR10 dataset using ResNet50. The third row displays Grad-CAM activation maps on the Flowers dataset using ResNet50.
  • Figure 3: AVCAVP accuracy when changing the hyperparameters of the affine transformation on the CIFAR10 dataset.
  • Figure 4: AVCAVP accuracy when changing the hyperparameters of the affine transformation on the Flowers dataset.
  • Figure 5: AVCAVP accuracy when changing the hyperparameter of the color transformation on the CIFAR10 dataset.
  • ...and 1 more figures

Theorems & Definitions (4)

  • Theorem B.1: cai2024sample
  • Proposition B.2
  • proof
  • Proposition B.3