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Exploring diverse and fine-grained caption for video by incorporating convolutional architecture into LSTM-based model

  • Published:2020-01 Issue: Volume:129 Page:173-180
  • ISSN:0167-8655
  • Container-title:Pattern Recognition Letters
  • language:en
  • Short-container-title:Pattern Recognition Letters

Author:

Xiao Huanhou,Xu Junwei,Shi Jinglun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference33 articles.

1. Convolutional image captioning;Aneja,2018

2. Hierarchical boundary-aware neural encoder for video captioning;Baraldi,2017

3. Collecting highly parallel data for paraphrase evaluation;Chen,2011

4. Microsoft coco captions: data collection and evaluation server;Chen,2015

5. Learning deep spatiotemporal features for video captioning;Daskalakis;Pattern Recognit. Lett.,2018

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1. Video-Captioning Evaluation Metric for Segments (VEMS): A Metric for Segment-level Evaluation of Video Captions with Weighted Frames;Multimedia Tools and Applications;2023-10-28

2. Multi-sentence video captioning using spatial saliency of video frames and content-oriented beam search algorithm;Expert Systems with Applications;2023-10

3. Video Captioning using Pre-Trained CNN and LSTM;2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT);2023-05-25

4. Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images;2023 11th International Workshop on Biometrics and Forensics (IWBF);2023-04-19

5. VATEX2020: pLSTM framework for video captioning;Procedia Computer Science;2023
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