SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning-Reference-Cited by-同舟云学术

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SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Lin Kevin¹,Li Linjie¹,Lin Chung-Ching¹,Ahmed Faisal¹,Gan Zhe¹,Liu Zicheng¹,Lu Yumao¹,Wang Lijuan¹

Affiliation:

1. Microsoft

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879057.pdf?arnumber=9879057

Reference61 articles.

1. Memory-Attended Recurrent Network for Video Captioning

2. Support-set bottlenecks for video-text representation learning;patrick;ICLRE,2021

3. End-to-End Learning of Visual Representations From Uncurated Instructional Videos

4. Clip4clip: An empirical study of clip for end to end video clip retrieval;luo;ArXiv Preprint,2021

5. Univl: A unified video and language pre-training model for multimodal understanding and generation;luo;ArXiv Preprint,2020

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