Recurrent Highway Networks with Language CNN for Image Captioning

By Jiuxiang Gu, G. Wang, Tsuhan Chen. 2016

11 citations recorded by the source corpus.

How researchers see this paper

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

1. Qiuhong Ke
2. Jun Liu
3. Mohammed Bennamoun
4. Ferdous Sohel
5. Lin Ma
6. Senjian An
7. Farid Boussaïd
8. Wenhao Jiang

Citations in the corpus

• Recent advances in convolutional neural networks (2015, neutral): “[335] combine the language CNN with recurrent highway networks and achieve a huge improvement compared to LSTM-based methods.”
• A New Representation of Skeleton Sequences for 3D Action Recognition (2017, background): “Although LSTM networks are designed to explore the long-term temporal dependency problem, it is still difficult for LSTM to memorize the information of the entire sequence with many timesteps [48, 13].”
• Recurrent Fusion Network for Image Captioning (2018, methodology): “We compare our proposed RFNet with the state-of-the-art approaches on image captioning, including Neural Image Caption (NIC) [18], Attribute LSTM [23], LSTM-A3 [24], Recurrent Image Captioner (RIC) [46], Recurrent Highway Network (RHN) [47], Soft Attention model [12], Attribute Attention model [48],”
• Learning Clip Representations for Skeleton-Based 3D Action Recognition (2018, background): “still difficult for LSTM to memorize the information of the entire sequence with many timesteps [26], [27].”