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New framework enhances federated cross-modal retrieval with missing modalities

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RCSR, a new framework designed to improve federated cross-modal retrieval, particularly when dealing with data heterogeneity and missing modalities across clients. The system utilizes a frozen CLIP backbone, incorporating shared adapters for global knowledge transfer and optional client-specific adapters for personalization. RCSR employs prototype anchoring to help unimodal clients align with global semantics and a semantic router on the server to dynamically adjust aggregation weights, enhancing both overall retrieval accuracy and training stability. AI

IMPACT Improves cross-modal retrieval accuracy and stability in federated learning scenarios with heterogeneous and incomplete data.

RANK_REASON This is a research paper detailing a new framework for federated cross-modal retrieval.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enhances federated cross-modal retrieval with missing modalities

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This is a research paper detailing a new framework for federated cross-modal retrieval.
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hefeng Zhou, Xuan Liu, Sicheng Chen, Wutong Zhang, Wu Yan, Jiong Lou, Chentao Wu, Guangtao Xue, Wei Zhao, Jie Li ·

    Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization

    arXiv:2604.22885v1 Announce Type: new Abstract: Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modalities. Under such heterogeneity, a single global model is often insufficient to cap…

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