Existing pre-trained vision-language models, e.g., CLIP, have demonstrated
impressive zero-shot generalization capabilities in various downstream tasks.
However, the performance of these models will degrade significantly when test
inputs present different distributions. To this end, we explore the concept of
test-time prompt tuning (TTPT), which enables the adaptation of the CLIP model
to novel downstream tasks through only one step of optimization on an
unsupervised objective that involves the test sample. Motivated by in-context
learning within field of natural language processing (NLP), we propose
In-Context Prompt Learning (InCPL) for test-time visual recognition task. InCPL
involves associating a new test sample with very few or even just one labeled
example as its in-context prompt. As a result, it can reliably estimate a label
for the test sample, thereby facilitating the model adaptation process. InCPL
first employs a token net to represent language descriptions as visual prompts
that the vision encoder of a CLIP model can comprehend. Paired with in-context
examples, we further propose a context-aware unsupervised loss to optimize test
sample-aware visual prompts. This optimization allows a pre-trained, frozen
CLIP model to be adapted to a test sample from any task using its learned
adaptive prompt. Our method has demonstrated superior performance and achieved
state-of-the-art results across various downstream datasets.

通过测试样本的无监督目标，在视觉识别任务中使用上下文提示学习来适应预训练的视觉 - 语言模型，并取得了在各种下游数据集上的有效结果。

冻结视觉语言模型的测试时视觉识别中的上下文提示学习

In-context Prompt Learning for Test-time Vision Recognition with Frozen  Vision-language Model

Benefiting from prompt tuning, recent years have witnessed the promising
performance of pre-trained vision-language models, e.g., CLIP, on versatile
downstream tasks. In this paper, we focus on a particular setting of learning
adaptive prompts on the fly for each test sample from an unseen new domain,
which is known as test-time prompt tuning (TPT). Existing TPT methods typically
rely on data augmentation and confidence selection. However, conventional data
augmentation techniques, e.g., random resized crops, suffers from the lack of
data diversity, while entropy-based confidence selection alone is not
sufficient to guarantee prediction fidelity. To address these issues, we
propose a novel TPT method, named DiffTPT, which leverages pre-trained
diffusion models to generate diverse and informative new data. Specifically, we
incorporate augmented data by both conventional method and pre-trained stable
diffusion to exploit their respective merits, improving the models ability to
adapt to unknown new test data. Moreover, to ensure the prediction fidelity of
generated data, we introduce a cosine similarity-based filtration technique to
select the generated data with higher similarity to the single test sample. Our
experiments on test datasets with distribution shifts and unseen categories
demonstrate that DiffTPT improves the zero-shot accuracy by an average of
5.13\% compared to the state-of-the-art TPT method. Our code and models will be
publicly released.

使用 DiffTPT 方法通过利用预训练扩散模型生成多样且信息丰富的新数据，结合常规方法和预训练稳定扩散的增强数据，选择与单个测试样本更相似的生成数据以提高预测准确度，证明 DiffTPT 方法能够提高零样本准确率。

有效测试时间提示调整的多样化数据增强与扩散

Diverse Data Augmentation with Diffusions for Effective Test-time Prompt  Tuning

Pre-trained vision-language models (e.g., CLIP) have shown promising
zero-shot generalization in many downstream tasks with properly designed text
prompts. Instead of relying on hand-engineered prompts, recent works learn
prompts using the training data from downstream tasks. While effective,
training on domain-specific data reduces a model's generalization capability to
unseen new domains. In this work, we propose test-time prompt tuning (TPT), a
method that can learn adaptive prompts on the fly with a single test sample.
For image classification, TPT optimizes the prompt by minimizing the entropy
with confidence selection so that the model has consistent predictions across
different augmented views of each test sample. In evaluating generalization to
natural distribution shifts, TPT improves the zero-shot top-1 accuracy of CLIP
by 3.6% on average, surpassing previous prompt tuning approaches that require
additional task-specific training data. In evaluating cross-dataset
generalization with unseen categories, TPT performs on par with the
state-of-the-art approaches that use additional training data. Project page:
this https URL

本文提出一种叫做测试时提示调整 (TPT) 的方法，可在单个测试样本上实时学习适应性提示，优化提前提供的 CLIP 模型动态调整提示， 以最小化模型预测时的不确定度，提高 CLIP 模型的泛化能力。实验结果表明，TPT 方法在自然情况下的 zero-shot top-1 准确率比以往方法的提升 3.6％，并达到了使用额外培训数据的最新先进方法的性能水平。