論文閱讀筆記：Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy
論文PDF：Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy 摘要 在本文中，提出了一個通用的無參考VQA框架，該框架基於帶卷積神經網絡（CNN）和重採樣策略的弱監督學習。 首先，通過弱監督學習來訓練八層CNN，以構造視頻塊的3D離散餘弦變換的變形與通過全參考（FR）VQA
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相關文章
- 1. 文獻閱讀筆記5 On the Use of Deep Learning for Blind Image Quality Assessment（2D）
- 2. Image Quality Assessment,IQA and Video Quality Assessment,VQA
- 3. 論文閱讀筆記 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
- 4. 論文閱讀筆記：A brief introduction to weakly supervised learning
- 5. [CVPR2017] Weakly Supervised Cascaded Convolutional Networks論文筆記
- 6. 論文筆記《Blind Quality Assessment Based on Pseudo-Reference Image》(1)
- 7. 論文筆記《Blind Quality Assessment Based on Pseudo-Reference Image》(2)
- 8. 圖像質量評價(IQA)論文筆記：Blind Image Quality Assessment via Deep Learning
- 9. [閱讀筆記]Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and C
- 10. RankIQA:Learning from Rankings for No-reference Image Quality Assessment論文閱讀筆記