WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization

• URL: http://arxiv.org/abs/2405.18405v1
• Date: Tue, 28 May 2024 17:46:27 GMT
• Title: WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization
• Authors: Jiawei Ma, Yulei Niu, Shiyuan Huang, Guangxing Han, Shih-Fu Chang,
• Abstract summary: We present WIDIn, Wording Images for Domain-Invariant representation, to disentangle discriminative visual representation. We first estimate the language embedding with fine-grained alignment, which can be used to adaptively identify and then remove domain-specific counterpart. We show that WIDIn can be applied to both pretrained vision-language models like CLIP, and separately trained uni-modal models like MoCo and BERT.
• Score: 63.98650220772378
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Language has been useful in extending the vision encoder to data from diverse distributions without empirical discovery in training domains. However, as the image description is mostly at coarse-grained level and ignores visual details, the resulted embeddings are still ineffective in overcoming complexity of domains at inference time. We present a self-supervision framework WIDIn, Wording Images for Domain-Invariant representation, to disentangle discriminative visual representation, by only leveraging data in a single domain and without any test prior. Specifically, for each image, we first estimate the language embedding with fine-grained alignment, which can be consequently used to adaptively identify and then remove domain-specific counterpart from the raw visual embedding. WIDIn can be applied to both pretrained vision-language models like CLIP, and separately trained uni-modal models like MoCo and BERT. Experimental studies on three domain generalization datasets demonstrate the effectiveness of our approach.

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