Latent Embeddings for Collective Activity Recognition
个人总结： 第一次看到这个方面的论文，方法上还是用了后验概率来建立模型，不过代码并没有开源，具体细节还要商榷。 摘要 提出的问题：传统的手工定制的特征只能定义有限范围内的关系。在一个集体内，个体之间复杂的依赖关系不能很好地建立模型。 解决方法：通过在特征空间（feature space）嵌入潜在变量（embedding latent variable），利用深度学习框架学习特征映射函数。 嵌入的潜
相关文章
- 1. Discriminative Embeddings of Latent Variable Models for Structured Data
- 2. [CVPR 2018笔记] 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论文阅读笔记
- 8. Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
- 9. Activity Recognition行为识别
- 10. Latent Space Policies for Hierarchical Reinforcement Learning
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • PHP开发工具
- • C# 中 foreach 遍历的用法
相关标签/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。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Discriminative Embeddings of Latent Variable Models for Structured Data
- 2. [CVPR 2018笔记] 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论文阅读笔记
- 8. Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
- 9. Activity Recognition行为识别
- 10. Latent Space Policies for Hierarchical Reinforcement Learning