Weakly-Supervised Action Localization by Generative Attention Modeling批註
Weakly-Supervised Action Localization by Generative Attention Modeling 主要任務 相關工作 方法： 主要任務 動作定位，方法又可以分爲兩類： 一、全監督學習方式：採用幀級標註，缺點在於耗時且浪費資源 二、弱監督學習方式：只在視頻級標註，改善了全監督的缺點 本文主要任務是：弱監督動作定位 (WSAL) 弱監督方式又分兩種： 1.自
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相關文章
- 1. Self-Attention Generative Adversarial Networks
- 2. SAGAN——Self-Attention Generative Adversarial Networks
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