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- W4385484802 abstract "Natural language video localization (NLVL) aims to locate the matching span relevant to a given query sentence from an untrimmed video. This task requires not only understanding video and text but also aligning the semantics between video and language. Existing methods obtain vision-language representations via separate encoders, cross-modal interactions are not fine-grained enough, and the semantics are not fully aligned. In this paper, we address the vision-language alignment via joint modeling and contrastive learning. We propose a unified Video-Language Representation Network (UniNet), employing a transformer encoder to learn vision-language representations aligned. Simultaneously taking video and text as input, the encoder jointly learns the representations of both and captures the inter-relations between video and text. Then the representations are used by the predictor to locate the grounding video span. Besides, we train our model with contrastive learning to enhance vision-language representations in the training stage. Experiments on three benchmark datasets show that UniNet outperforms the baseline methods and adopting unified representation and contrastive learning can improve vision-language semantic alignment." @default.
- W4385484802 created "2023-08-03" @default.
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- W4385484802 date "2023-06-18" @default.
- W4385484802 modified "2023-09-26" @default.
- W4385484802 title "Learning Unified Video-Language Representations via Joint Modeling and Contrastive Learning for Natural Language Video Localization" @default.
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- W4385484802 doi "https://doi.org/10.1109/ijcnn54540.2023.10191104" @default.
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