Latent Embeddings for Collective Activity Recognition
個人總結： 第一次看到這個方面的論文，方法上還是用了後驗概率來建立模型，不過代碼並沒有開源，具體細節還要商榷。 摘要 提出的問題：傳統的手工定製的特徵只能定義有限範圍內的關係。在一個集體內，個體之間複雜的依賴關係不能很好地建立模型。 解決方法：通過在特徵空間（feature space）嵌入潛在變量（embedding latent variable），利用深度學習框架學習特徵映射函數。 嵌入的潛
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相關文章
- 1. Discriminative Embeddings of Latent Variable Models for Structured Data
- 2. [CVPR 2018筆記] Discriminative Learning of Latent Features for Zero-Shot Recognition
- 3. [CVPR 18] Discriminative Learning of Latent Features for Zero-Shot Recognition
- 4. [CVPR 2018]Discriminative Learning of Latent Features for Zero-Shot Recognition
- 5. X-VECTOR:ROBUST DNN EMBEDDING FOR SPEAKER RECOGNITION
- 6. 《Programming Collective Intelligence》review
- 7. Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval論文閱讀筆記
- 8. Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
- 9. Activity Recognition行爲識別
- 10. Latent Space Policies for Hierarchical Reinforcement Learning