An empirical study of excitation and aggregation design adaptions in CLIP4Clip for video

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An empirical study of excitation and aggregation design adaptions in CLIP4Clip for video–text retrieval

Published:2024-09 Issue: Volume:596 Page:127905
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Jing Xiaolun^ORCID,Yang Genke^ORCID,Chu Jian

Publisher

Elsevier BV

Reference62 articles.

1. Improving video-text retrieval by multi-stream corpus alignment and dual softmax loss;Cheng,2021

2. Clip2video: Mastering video-text retrieval via image clip;Fang,2021

3. Visual spatio-temporal relation-enhanced network for cross-modal text-video retrieval;Han,2021

4. Recurrent neural network regularization;Zaremba,2014

5. Coot: Cooperative hierarchical transformer for video-text representation learning;Ging;Adv. Neural Inf. Process. Syst.,2020

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