Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval-Reference-Cited by-同舟云学术

Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval

Published:2018-06-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval
language:
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Author:

Mithun Niluthpol Chowdhury¹,Li Juncheng²,Metze Florian³,Roy-Chowdhury Amit K.¹

Affiliation:

1. University of California, Riverside, Riverside, CA, USA

2. Carnegie Mellon University & Bosch Research and Technology Center, Pittsburgh, PA, USA

3. Carnegie Mellon University, Pittsburgh, PA, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3206025.3206064

Reference42 articles.

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3. David L Chen and William B Dolan. 2011. Collecting highly parallel data for paraphrase evaluation Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. ACL 190--200. David L Chen and William B Dolan. 2011. Collecting highly parallel data for paraphrase evaluation Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. ACL 190--200.

4. Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014). Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

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