Ensemble Image and Text for Unsupervised Domain Adaptation Using Vision Language Models.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Unsupervised Domain Adaptation (UDA) aims to improve model performance on unlabeled target domains despite domain shifts.
• Aligning source and target domain distributions in a latent space is key but challenging due to distribution discrepancies.
• Suboptimal latent spaces can lead to semantic information loss, hindering task-specific feature representation.

Purpose of the Study:

• To address the challenges of identifying optimal latent spaces in UDA.
• To leverage the semantic abstraction capabilities of natural language to mitigate domain shifts.
• To propose a novel cross-modal translation model bridging the visual-semantic gap.

Main Methods:

• Systematic analysis revealing natural language's stronger semantic abstraction and smaller domain shifts compared to visual features.
• A novel model transforming visual patterns into structured linguistic representations.
• The model includes a text classification branch, an image adaptation branch, and an ensemble mechanism for reconciling modalities.

Main Results:

• The proposed cross-modal translation effectively mitigates domain shifts by utilizing language's invariant semantic properties.
• Task-critical semantic hierarchies are preserved, enhancing cross-domain generalization.
• State-of-the-art performance achieved on three benchmark datasets.

Conclusions:

• Natural language representations offer a powerful approach to reduce domain shifts in UDA.
• The proposed model successfully bridges the visual-semantic gap by leveraging linguistic structures.
• The approach enhances model robustness and performance in heterogeneous data scenarios.