Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation

TL;DR

This paper introduces IT3A, a multi-modal test-time adaptation method that uses generative models for data augmentation across text and images, significantly improving accuracy under distribution shifts.

Contribution

IT3A leverages pre-trained vision and language models for multi-modal augmentation and employs cosine similarity filtering, offering a novel approach to test-time adaptation beyond single-modality methods.

Findings

Outperforms state-of-the-art TPT methods by 5.50% in accuracy.

Effectively filters spurious augmentations using cosine similarity.

Enhances model robustness to distribution shifts and domain gaps.

Abstract

Existing test-time prompt tuning (TPT) methods focus on single-modality data, primarily enhancing images and using confidence ratings to filter out inaccurate images. However, while image generation models can produce visually diverse images, single-modality data enhancement techniques still fail to capture the comprehensive knowledge provided by different modalities. Additionally, we note that the performance of TPT-based methods drops significantly when the number of augmented images is limited, which is not unusual given the computational expense of generative augmentation. To address these issues, we introduce IT3A, a novel test-time adaptation method that utilizes a pre-trained generative model for multi-modal augmentation of each test sample from unknown new domains. By combining augmented data from pre-trained vision and language models, we enhance the ability of the model to adapt to unknown new test data. Additionally, to ensure that key semantics are accurately retained when generating various visual and text enhancements, we employ cosine similarity filtering between the logits of the enhanced images and text with the original test data. This process allows us to filter out some spurious augmentation and inadequate combinations. To leverage the diverse enhancements provided by the generation model across different modals, we have replaced prompt tuning with an adapter for greater flexibility in utilizing text templates. Our experiments on the test datasets with distribution shifts and domain gaps show that in a zero-shot setting, IT3A outperforms state-of-the-art test-time prompt tuning methods with a 5.50% increase in accuracy.