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Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models

Yongjin Yang, Jongwoo Ko, Se-Young Yun

TL;DR

This paper empirically analyze how each ETL method behaves with respect to transfer difficulty and proposes an adaptive ensemble method that combines visual prompts and text adapters with pre-trained VLMs, tailored by transfer difficulty, to achieve optimal performance for any target domain.

Abstract

Vision-language models (VLMs) like CLIP have demonstrated remarkable applicability across a variety of downstream tasks, including zero-shot image classification. Recently, the use of prompts or adapters for efficient transfer learning (ETL) has gained significant attention for effectively adapting to downstream tasks. However, previous studies have overlooked the challenge of varying transfer difficulty of downstream tasks. In this paper, we empirically analyze how each ETL method behaves with respect to transfer difficulty. Our observations indicate that utilizing vision prompts and text adapters is crucial for adaptability and generalizability in domains with high difficulty. Also, by applying an adaptive ensemble approach that integrates task-adapted VLMs with pre-trained VLMs and strategically leverages more general knowledge in low-difficulty and less in high-difficulty domains, we consistently enhance performance across both types of domains. Based on these observations, we propose an adaptive ensemble method that combines visual prompts and text adapters with pre-trained VLMs, tailored by transfer difficulty, to achieve optimal performance for any target domain. Upon experimenting with extensive benchmarks, our method consistently outperforms all baselines, particularly on unseen tasks, demonstrating its effectiveness.

Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models

TL;DR

This paper empirically analyze how each ETL method behaves with respect to transfer difficulty and proposes an adaptive ensemble method that combines visual prompts and text adapters with pre-trained VLMs, tailored by transfer difficulty, to achieve optimal performance for any target domain.

Abstract

Vision-language models (VLMs) like CLIP have demonstrated remarkable applicability across a variety of downstream tasks, including zero-shot image classification. Recently, the use of prompts or adapters for efficient transfer learning (ETL) has gained significant attention for effectively adapting to downstream tasks. However, previous studies have overlooked the challenge of varying transfer difficulty of downstream tasks. In this paper, we empirically analyze how each ETL method behaves with respect to transfer difficulty. Our observations indicate that utilizing vision prompts and text adapters is crucial for adaptability and generalizability in domains with high difficulty. Also, by applying an adaptive ensemble approach that integrates task-adapted VLMs with pre-trained VLMs and strategically leverages more general knowledge in low-difficulty and less in high-difficulty domains, we consistently enhance performance across both types of domains. Based on these observations, we propose an adaptive ensemble method that combines visual prompts and text adapters with pre-trained VLMs, tailored by transfer difficulty, to achieve optimal performance for any target domain. Upon experimenting with extensive benchmarks, our method consistently outperforms all baselines, particularly on unseen tasks, demonstrating its effectiveness.
Paper Structure (43 sections, 14 equations, 12 figures, 17 tables, 2 algorithms)

This paper contains 43 sections, 14 equations, 12 figures, 17 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Backgrounds
  3. Zero-shot CLIP.
  4. Prompt Tuning for CLIP.
  5. Adapter-style Tuning for CLIP.
  6. Motivating Observations
  7. Method
  8. Configuration Design & Training
  9. Adaptive Ensemble for Evaluation
  10. Vision Ensemble.
  11. Experiments
  12. Experimental Setup
  13. Datasets.
  14. Experimental Details.
  15. Main Results
  16. ...and 28 more sections

Figures (12)

  • Figure 1: Overview of APEX compared to the conventional ETL methods. APEX exhibits two key differences: (a):Firstly, APEX integrates prompt tuning for the visual encoder and a linear adapter for the text encoder, each tailored to the specific properties of their respective modalities, which performs better on high-difficulty domains. (b):Secondly, APEX integrates an adaptive coefficient within the text encoder to strategically balance pre-adapter and post-adapter features to properly combine task-specific knowledge and general VLMs knowledge based on transfer difficulty. A detailed explanation, including notations and the algorithm, can be found in Section \ref{['sec:method']} and Appendix \ref{['appendix:notation_algorithm']}.
  • Figure 2: Comparison of accuracy differences (%) between base and novel categories across three prompt tuning options (TPT, VPT+TPT, VPT) with varying numbers of shots.
  • Figure 3: Comparison of the accuracy (%) of base and novel categories using TPT, VPT, and their combination (VPT+TPT) on three transfer learning datasets over various training epochs.
  • Figure 4: t-SNE van2008visualizing plots of visual features for novel category with their corresponding labels (left), zero-shot CLIP prediction (middle), and prediction with TPT (right). 50 samples are randomly selected from each class in EuroSAT and SUN397, using all 5 classes in EuroSAT and 5 randomly chosen classes from SUN397. Dotted lines within the t-SNE plot represent the decision boundaries corresponding to each class, indicated by the same color.
  • Figure 5: Comparison of intra- and inter-class ratios to show class separability across different datasets with their RTD, arranged from low to high RTD.
  • ...and 7 more figures

Theorems & Definitions (1)

  • Definition 1: Relative Transfer Difficulty yu2023task