論文筆記 Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders
本文提出了一個視頻精彩片段檢測算法 之前的精彩視頻剪輯的方法常用的是處於監督學習或者啓發式規則下，本文采用的無監督學習的方法，同時也藉助到Youtube上各種各樣的用戶剪輯過的視頻用於訓練，同時作者考慮到在用戶剪輯過的視頻中，精彩部分出現的頻率和時長肯定會大於不是那麼精彩部分出現的時長。 視頻的highlights存在着三個巨大的挑戰，第一個是儘管大部分人對於highlight有着相同的概念但是出
相關文章
- 1. 05論文筆記《Video Super-Resolution via Bidirectional Recurrent Convolutional Networks》
- 2. Joint Event Extraction via Recurrent Neural Networks
- 3. CVPR 2017【論文筆記】Unsupervised Learning of Depth and Ego-Motion from Video
- 4. 【論文筆記】Unsupervised Learning of Video Representations using LSTMs
- 5. 《Unsupervised Learning of Depth and Ego-Motion from Video》論文筆記
- 6. 【論文筆記】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
- 7. JRNN: Joint Event Extraction via Recurrent Neural Networks
- 8. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings論文筆記
- 9. 【論文筆記】Unsupervised Information Extraction: Regularizing Discriminative Approaches with RelDist loss
- 10. [CVPR 2019 論文筆記] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
- 更多相關文章...
- • XSLT
- • CAP理論是什麼？ - NoSQL教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • Scala 中文亂碼解決
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 05論文筆記《Video Super-Resolution via Bidirectional Recurrent Convolutional Networks》
- 2. Joint Event Extraction via Recurrent Neural Networks
- 3. CVPR 2017【論文筆記】Unsupervised Learning of Depth and Ego-Motion from Video
- 4. 【論文筆記】Unsupervised Learning of Video Representations using LSTMs
- 5. 《Unsupervised Learning of Depth and Ego-Motion from Video》論文筆記
- 6. 【論文筆記】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
- 7. JRNN: Joint Event Extraction via Recurrent Neural Networks
- 8. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings論文筆記
- 9. 【論文筆記】Unsupervised Information Extraction: Regularizing Discriminative Approaches with RelDist loss
- 10. [CVPR 2019 論文筆記] Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders