Weakly-Supervised Action Localization by Generative Attention Modeling批注

时间 2021-01-04

标签人工智能机器学习繁體版

原文原文链接

Weakly-Supervised Action Localization by Generative Attention Modeling 主要任务相关工作方法：主要任务动作定位，方法又可以分为两类：一、全监督学习方式：采用帧级标注，缺点在于耗时且浪费资源二、弱监督学习方式：只在视频级标注，改善了全监督的缺点本文主要任务是：弱监督动作定位 (WSAL) 弱监督方式又分两种： 1.自

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1. Self-Attention Generative Adversarial Networks
2. SAGAN——Self-Attention Generative Adversarial Networks
3. ICMR 19：Temporal Activity Localization by Language
4. 论文笔记（四）：Temporal Action Localization by Structured Maximal Sums
5. DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks阅读笔记
6. Multiway Attention Networks for Modeling Sentence Pairs
7. Graph Convolutional Networks for Temporal Action Localization
8. P-GCN：Graph Convolutional Networks for Temporal Action Localization 2019 ICCV
9. DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks
10. Hierarchical Generative Modeling for Controllable Speech Synthesis笔记

>>更多相关文章<<