most citedAligning where to see and what to tell: image caption with region-based attention and scene factorization

107 citations · 119 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20201 cited

Road Network Metric Learning for Estimated Time of Arrival

Yiwen Sun, Kun Fu, Zheng Wang +2

Recently, deep learning have achieved promising results in Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the origin to the destination alo…

cs.LG20204 cited

FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention

Yiwen Sun, Yulu Wang, Kun Fu +4

Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in rece…

cs.LG20204 cited

Fusion Recurrent Neural Network

Yiwen Sun, Yulu Wang, Kun Fu +3

Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will t…

cs.LG20203 cited

Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting

Yiwen Sun, Yulu Wang, Kun Fu +3

Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is cha…

cs.CV2015107 cited

Aligning where to see and what to tell: image caption with region-based attention and scene factorization

Junqi Jin, Kun Fu, Runpeng Cui +2

Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sen…