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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 ConSLT: A Token-level Contrastive Framework for Sign Language Translation ... Fu, Biao; Ye, Peigen; Zhang, Liang. - : arXiv, 2022 BASE Show details 2 Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity ... Zhang, He; Zhang, Liang; Lin, Ang. - : arXiv, 2020 BASE Show details 3 Self-supervised learning to detect key frames in videos Yan, Xiang; Gilani, Syed Zulqarnain; Feng, Mingtao; Zhang, Liang; Qin, Hanlin; Mian, Ajmal In: Research outputs 2014 to 2021 (2020) Abstract: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large corpus of training data to train the models. Labelling requires human annotators from different backgrounds to annotate key frames in videos which is not only expensive and time consuming but also prone to subjective errors and inconsistencies between the labelers. To overcome these problems, we propose an automatic self-supervised method for detecting key frames in a video. Our method comprises a two-stream ConvNet and a novel automatic annotation architecture able to reliably annotate key frames in a video for self-supervised learning of the ConvNet. The proposed ConvNet learns deep appearance and motion features to detect frames that are unique. The trained network is then able to detect key frames in test videos. Extensive experiments on UCF101 human action and video summarization VSUMM datasets demonstrates the effectiveness of our proposed method. Keyword: Broadcast and Video Studies; Communication; Computer Sciences; Convolutional networks; Key frames; Physical Sciences and Mathematics; Self-supervised learning; Social and Behavioral Sciences; Systems Architecture; Two-stream ConvNets URL: https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=10392&context=ecuworkspost2013 https://ro.ecu.edu.au/ecuworkspost2013/9385 BASE Hide details 4 Multiple-instance multiple-label learning for the classification of frog calls with acoustic event detection Xie, Jie; Towsey, Michael; Zhang, Liang. - : Springer, 2016 BASE Show details 5 Nao-Xue-Shu Oral Liquid Improves Aphasia of Mixed Stroke Yan, Yuping; Wang, Mingzhe; Zhang, Liang. - : Hindawi Publishing Corporation, 2015 BASE Show details 6 EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things [<Journal>] Zhou, ZhangBing [Verfasser]; Tang, Jine [Verfasser]; Zhang, Liang-Jie [Verfasser]. DNB Subject Category Language Show details

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