Quaddirectional 2D-Recurrent Neural Networks For Image Labeling

By Bing Shuai, Zhen Zuo, G. Wang. 2015

35 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. G. Wang
2. Jianfei Cai
3. Jiuxiang Gu
4. Haibin Ling
5. Amir Shahroudy
6. Jun Liu
7. Heng Fan
8. Bing Shuai

Citations in the corpus

• Recent advances in convolutional neural networks (2015, background): “[189] and [190] use the recurrent neural networks to model the contextual dependencies among image features from CNNs, and dramatically boost the labeling performance.”
• Recent Advances in Recurrent Neural Networks (2017, methodology): “This method efficiently embeds the global spatial context into the compact local representation [49].”
• Dense Recurrent Neural Networks for Scene Labeling (2018, methodology): “parsing trees for sentences) is beneficial for modeling the global representation of sentences in NLP. Our proposed DAG-RNN is a generalization of chain-RNNs [1][12], tree-RNNs [29][42] and 2D-RNNs [9][26], and enable the network to model long-range semantic dependencies for graphical structured imag”
• DAG-Recurrent Neural Networks for Scene Labeling (2015, methodology): “In our experiment, we use the four context propagation directions (southeast, southwest, northwest and northeast) suggested by [9][23] to decompose the UCG.”