see the forest for the trees：spitial and temporal recurrent neural networks for video-based re-id
这是一篇来自2017年的中科院自动化所的论文，也是导师让读的3篇神经网络与多任务学习的3篇论文之一。读的不是很懂，把主要方法记录一下。 这篇论文采用三元组的输入形式，以序列3元组的形式去计算损失函数。在使用CNN去提取特征之后，分别通过时序RNN去学习特征，和图像序列组合后通过空间RNN来进行度量学习。 下图是本文的基本结构和框架： 从总体框架来看，这篇和之前看的论文的孪生网络+RNN提取特征的方
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相关文章
- 1. The Unreasonable Effectiveness of Recurrent Neural Networks
- 2. Taming Recurrent Neural Networks for Better Summarization
- 3. Recurrent Convolutional Neural Networks for Text Classification
- 4. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
- 5. Attention and Augmented Recurrent Neural Networks
- 6. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
- 7. Disconnected Recurrent Neural Networks for Text Categorization
- 8. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
- 9. Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
- 10. Summary - CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly