Unified modality separation: A vision-language framework for unsupervised domain adaptation

TL;DR

This paper introduces a unified modality separation framework for unsupervised domain adaptation using vision-language models, effectively disentangling modality-specific and invariant features to improve target domain performance.

Contribution

It proposes a novel modality separation approach that handles modality-specific and invariant features separately, with adaptive weighting and a new discrepancy metric for better domain adaptation.

Findings

Achieves up to 9% performance improvement.

Provides 9 times computational efficiency.

Demonstrates effectiveness across multiple datasets and backbones.

Abstract

Unsupervised domain adaptation (UDA) enables models trained on a labeled source domain to handle new unlabeled domains. Recently, pre-trained vision-language models (VLMs) have demonstrated promising zero-shot performance by leveraging semantic information to facilitate target tasks. By aligning vision and text embeddings, VLMs have shown notable success in bridging domain gaps. However, inherent differences naturally exist between modalities, which is known as modality gap. Our findings reveal that direct UDA with the presence of modality gap only transfers modality-invariant knowledge, leading to suboptimal target performance. To address this limitation, we propose a unified modality separation framework that accommodates both modality-specific and modality-invariant components. During training, different modality components are disentangled from VLM features then handled separately in a unified manner. At test time, modality-adaptive ensemble weights are automatically determined to maximize the synergy of different components. To evaluate instance-level modality characteristics, we design a modality discrepancy metric to categorize samples into modality-invariant, modality-specific, and uncertain ones. The modality-invariant samples are exploited to facilitate cross-modal alignment, while uncertain ones are annotated to enhance model capabilities. Building upon prompt tuning techniques, our methods achieve up to 9% performance gain with 9 times of computational efficiencies. Extensive experiments and analysis across various backbones, baselines, datasets and adaptation settings demonstrate the efficacy of our design.