Open-set Mixed Domain Adaptation via visual-linguistic focal evolving

Publication Type

Journal Article

Publication Date

9-2025

Abstract

We introduce a new task, Open-set Mixed Domain Adaptation (OSMDA), which considers the potential mixture of multiple distributions in the target domains, thereby better simulating real-world scenarios. To tackle the semantic ambiguity arising from multiple domains, our key idea is that the linguistic representation can serve as a universal descriptor for samples of the same category across various domains. We thus propose a more practical framework for cross-domain recognition via visual-linguistic guidance. On the other hand, the presence of multiple domains also poses a new challenge in classifying both known and unknown categories. To combat this issue, we further introduce a visual-linguistic focal evolving approach to gradually enhance the classification ability of a known/unknown binary classifier from two aspects. Specifically, we start with identifying highly confident focal samples to expand the pool of known samples by incorporating those from different domains. Then, we amplify the feature discrepancy between known and unknown samples through dynamic entropy evolving via an adaptive entropies min/max game, enabling us to accurately identify possible unknown samples in a gradual manner. Extensive experiments demonstrate our method’s superiority against the state-of-the-arts in both open-set and open-set mixed domain adaptation.

Keywords

Domain adaptation, mixed domain, open-set, visual-linguistics model, entropy evolving

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Circuits and Systems for Video Technology

Volume

35

Issue

9

First Page

8495

Last Page

8507

ISSN

1051-8215

Identifier

10.1109/TCSVT.2025.3551234

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TCSVT.2025.3551234

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