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New network improves video moment retrieval across domains

Researchers have introduced a novel Multi-Modal Cross-Domain Alignment (MMCDA) network designed to improve video moment retrieval across different datasets. This approach addresses the challenge of performance degradation when models trained on one domain are applied to another, particularly when the target domain lacks annotations. The MMCDA network incorporates domain alignment, cross-modal alignment, and specific alignment modules to learn domain-invariant and semantically aligned representations, enabling effective knowledge transfer from annotated source domains to unannotated target domains. AI

IMPACT Introduces a method to improve cross-domain generalization for video retrieval tasks, potentially reducing the need for extensive manual annotation in new domains.

RANK_REASON This is a research paper describing a novel network for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New network improves video moment retrieval across domains

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This is a research paper describing a novel network for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiang Fang, Daizong Liu, Pan Zhou, Yuchong Hu ·

    Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

    arXiv:2209.11572v3 Announce Type: replace-cross Abstract: As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend…