Unsupervised Prompt Learning for Vision-Language Models (2204.03649v2)

Abstract: Contrastive vision-LLMs like CLIP have shown great progress in transfer learning. In the inference stage, the proper text description, also known as prompt, needs to be carefully designed to correctly classify the given images. In order to avoid laborious prompt engineering, recent works such as CoOp, CLIP-Adapter and Tip-Adapter propose to adapt vision-LLMs for downstream image recognition tasks on a small set of labeled data. Though promising improvements are achieved, requiring labeled data from the target datasets may restrict the scalability. In this paper, we explore a different scenario, in which the labels of the target datasets are unprovided, and we present an unsupervised prompt learning (UPL) approach to avoid prompt engineering while simultaneously improving transfer performance of CLIP-like vision-LLMs. As far as we know, UPL is the first work to introduce unsupervised learning into prompt learning. Experimentally, our UPL outperforms original CLIP with prompt engineering on ImageNet as well as other 10 datasets. An enhanced version of UPL is even competitive with the 8-shot CoOp and the 8-shot TIP-Adapter on most datasets. Code and models are available at https://github.com/tonyhuang2022/UPL.

Unsupervised Prompt Learning for Vision-LLMs: An Expert Overview

The paper "Unsupervised Prompt Learning for Vision-LLMs" introduces a novel approach, named Unsupervised Prompt Learning (UPL), specifically targeting vision-LLMs such as CLIP. This research presents an unsupervised alternative to traditional supervised prompt engineering techniques by leveraging pseudo-labeling and a self-training strategy to enhance model performance in downstream visual recognition tasks.

Key Focus and Methodology

The paper primarily addresses the challenge of labor-intensive prompt engineering, which is essential for fine-tuning vision-LLMs in image classification tasks. Vision-LLMs, including CLIP, ALIGN, and FLIP, operate by aligning images with text in a shared embedding space, necessitating carefully curated text prompts to achieve optimal task performance.

1. Unsupervised Prompt Learning (UPL): UPL introduces an unsupervised framework that bypasses the need for labeled data in downstream tasks, unlike previous models that require supervised learning paradigms. This is achieved by generating pseudo-labels for the dataset using pre-trained CLIP models, thereby enabling prompt learning without explicit human-labeled datasets.
2. Pseudo-label Generation and Optimization: The pseudo-labels are generated based on the confidence scores derived from the CLIP model predictions. UPL adopts a top-K sampling strategy, selecting the most confident K samples per class instead of traditional threshold-based selections, to build a pseudo-labeled dataset. This strategy mitigates the propensity of imbalanced distributions observed in threshold-based selections due to variations in class preferences exhibited by CLIP models.
3. Robust Self-Training Procedure: The paper implements a self-training mechanism that optimizes learnable prompt representations using pseudo-labeled samples. These learnable prompts replace hand-crafted templates, integrating closely with the text encoders of vision-LLMs for improved task-specific tuning.

Experimental Evidence and Comparison

The experimental results showcase that both UPL and its enhanced version, UPL*, outperform the original CLIP using prompt engineering across several benchmarks, including ImageNet and ten other datasets. The performance of UPL is particularly notable when achieving competitive results with supervised approaches like CoOp and Tip-Adapter, even when these methods use a few-shot learning strategy (2-shot or 8-shot), highlighting UPL's effectiveness despite the absence of labeled data.

Implications and Future Directions

The employment of UPL yields several implications for both practical applications and theoretical advancements:

• Scalable and Efficient Learning: UPL offers scalability as it eliminates dependency on labeled data, facilitating broader applicability across diverse and evolving datasets without incurring the costs associated with labeling.
• Enhanced Transferability of Vision-LLMs: The incorporation of UPL within the training pipeline of vision-LLMs can potentially harmonize model transferability, ensuring robust performance across diverse domains and tasks.
• Foundation for Further Research: The introduction of unsupervised learning into prompt optimization may inspire future research addressing domain adaptation, model robustness, and efficient model tuning strategies.

In essence, this paper establishes a new frontier in vision-language interactions, emphasizing unsupervised learning paradigms to alleviate traditional constraints linked with prompt design. While achieving promising results, it also opens avenues for explorations into generalized AI frameworks where models efficiently transfer learning with minimal human intervention.