Abstract
Multi-Source Domain Adaptation (MSDA) aims to mitigate changes in data distribution when transferring knowledge from multiple labeled source domains to an unlabeled target domain. However, existing MSDA techniques assume target domain images are available, yet overlook image-rich semantic information. Consequently, an open question is whether MSDA can be guided solely by textual cues in the absence of target domain images. By employing a multimodal model with a joint image and language embedding space, we propose a novel language-guided MSDA approach, termed LanDA, based on optimal transfer theory, which facilitates the transfer of multiple source domains to a new target domain, requiring only a textual description of the target domain without needing even a single target domain image, while retaining task-relevant information. We present extensive experiments across different transfer scenarios using a suite of relevant benchmarks, demonstrating that LanDA outperforms standard fine-tuning and ensemble approaches in both target and source domains.
Overview
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The paper introduces LanDA, a method for Multi-Source Domain Adaptation (MSDA) using textual descriptions instead of domain images.
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LanDA utilizes Optimal Transport (OT) theory and Visual-Language Foundational Models (VLFMs) for domain adaptation.
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The method does not need target domain images, instead leveraging language descriptions for domain alignment.
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LanDA freezes the parameters of existing models like CLIP and employs augmenters for transforming image embeddings into extended domains for alignment.
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Extensive experiments confirm LanDA's superior accuracy over traditional fine-tuning and ensemble methods in domain adaptation tasks.
Overview of LanDA: Language-Guided Multi-Source Domain Adaptation
Multi-Source Domain Adaptation (MSDA) remains a challenging area within AI, particularly due to the reliance on domain images to guide the adaptation process. A paper introduces a groundbreaking method known as LanDA (Language-Guided Multi-Source Domain Adaptation) that pivots from conventional approaches by exclusively leveraging textual descriptions. LanDA engages with Optimal Transport (OT) theory and Visual-Language Foundational Models (VLFMs) to adapt multiple source domains to a target domain without requiring target domain images.
Challenges and Novel Approach
Traditional MSDA methods necessitate target domain images for successful adaptation, which can be problematic when such images are hard to obtain. LanDA circumvents this by exploiting language descriptions of the target domain, thus removing the need for actual target domain imagery. This is enabled by LanDA's utilization of a VLFMs framework, which has a joint image and language embedding space allowing for effective domain alignment based on language cues.
Mechanisms and Contributions
LanDA freezes the parameters of a model like CLIP and inserts lightweight augmenters to transform image embeddings from multiple source domains into extended domains. These extended domains are then projected into a Wasserstein space, accounting for both image and text information, to align with the unseen target domain and preserve class-specific attributes. To evaluate the effectiveness of LanDA, extensive experiments were carried out across varying transfer scenarios and benchmarks. The results demonstrate that LanDA achieves superior accuracy over standard fine-tuning and ensemble approaches, both in target and source domains.
Performance Evaluation and Future Outlook
The proposed LanDA method exhibits notable accuracy improvements, substantiating the viability of text-guided adaptation. It has the potential to shape future methodologies that aim to refine domain adaptation processes, especially given the often difficult task of collecting extensive target domain image datasets. The adaptability of LanDA alongside its methodological innovation offers a promising avenue for harnessing language as a guiding force in domain adaptation tasks. This paves the way for further exploration into methods that mitigate reliance on image datasets and exploit the synergy between language and vision modalities within AI.
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