Catching the Temporal Regions-of-Interest for Video Captioning-Reference-Cited by-同舟云学术

Catching the Temporal Regions-of-Interest for Video Captioning

Published:2017-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 25th ACM international conference on Multimedia
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Author:

Yang Ziwei¹,Han Yahong¹,Wang Zheng¹

Affiliation:

1. Tianjin University, Tianjin, China

Funder

Natural Science Foundation of China (NSFC)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3123266.3123327

Reference39 articles.

1. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate ICLR. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate ICLR.

2. Nicolas Ballas Li Yao Chris Pal and Aaron Courville. 2016. Delving deeper into convolutional networks for learning video representations ICLR. Nicolas Ballas Li Yao Chris Pal and Aaron Courville. 2016. Delving deeper into convolutional networks for learning video representations ICLR.

3. Lorenzo Baraldi Costantino Grana and Rita Cucchiara. 2017. Hierarchical Boundary-Aware Neural Encoder for Video Captioning CVPR. Lorenzo Baraldi Costantino Grana and Rita Cucchiara. 2017. Hierarchical Boundary-Aware Neural Encoder for Video Captioning CVPR.

4. Long Chen Hanwang Zhang Jun Xiao Liqiang Nie Jian Shao Wei Liu and Tat-Seng Chua. 2017. SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning CVPR. Long Chen Hanwang Zhang Jun Xiao Liqiang Nie Jian Shao Wei Liu and Tat-Seng Chua. 2017. SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning CVPR.

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